- AI for Trading
- Content
- Part 01 : Quantitative Trading
- Part 02 : AI Algorithms in Trading
- Part 03 (Elective): Python Refresher
- Part 04 (Elective): Linear Algebra
- Part 05 (Elective): Jupyter Notebook, Numpy, and Pandas
- Part 06 (Elective): Statistics
- Part 07 (Elective): Machine Learning
- Part 08 (Elective): Deep Learning
- Part 09 (Elective): Computer Vision
- Part 10 (Elective): Natural Language Processing
- Content
AI for Trading
助教微信
udacity公众号
Nanodegree key: nd880
Version: 6.0.0
Locale: en-us
Complete real-world projects designed by industry experts, covering topics from asset management to trading signal generation. Master AI algorithms for trading, and build your career-ready portfolio.
Content
Part 01 : Quantitative Trading
Learn the basics of quantitative analysis, including data processing, trading signal generation, and portfolio management. Use Python to work with historical stock data, develop trading strategies, and construct a multi-factor model with optimization.
- Module 01: Quant Basics
- Lesson 01: Welcome to the Nanodegree ProgramWelcome to the exciting world of Quantitative Trading! Say hello to your instructors and get an overview of the program.
- Lesson 02: Knowledge, Community, and CareersYou are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.
- Lesson 03: Get Help with Your AccountWhat to do if you have questions about your account or general questions about the program.
- Lesson 04: Stock PricesLearn about stocks and common terminology used when analyzing stocks.
- Lesson 05: Market MechanicsLearn about how modern stock markets function, how trades are executed and prices are set. Study market behavior, and analyze price and volume data to identify potential trading signals.
- Concept 01: Intro
- Concept 02: Farmers’ Market
- Concept 03: Trading Stocks
- Concept 04: Liquidity
- Concept 05: Tick Data
- Concept 06: OHLC: Open, High, Low, Close
- Concept 07: Quiz: Resample Data
- Concept 08: Volume
- Concept 09: Gaps in Market Data
- Concept 10: Markets in Different Timezones
- Concept 11: Summary
- Concept 12: Better Learning - By Sleeping
- Lesson 06: Data ProcessingLearn how to adjust market data for corporate actions, include fundamental information in your analysis and compute technical indicators.
- Concept 01: Market Data
- Concept 02: When to Use Time Stamps
- Concept 03: Corporate Actions: Stock Splits
- Concept 04: Technical Indicators
- Concept 05: Missing Values
- Concept 06: Trading Days
- Concept 07: Quiz: Trading Experiment
- Concept 08: Survivor Bias
- Concept 09: Fundamental Information
- Concept 10: Price Earnings Ratio
- Concept 11: Exchange Traded Funds
- Concept 12: Index vs ETF
- Concept 13: Alternative Data
- Concept 14: Interview: Satellite Data
- Concept 15: Interlude: Your Goals
- Lesson 07: Stock ReturnsLearn how to calculate stock returns, and log returns in particular. Learn why log returns are used to analyze financial data.
- Lesson 08: Momentum TradingLearn about alpha signals, and how they can be applied to a long/short trading strategy. Learn about momentum, a common alpha signal used in trading strategies.
- Concept 01: Designing a Trading Strategy
- Concept 02: Momentum-based Signals
- Concept 03: Quiz: Momentum-based Signals
- Concept 04: Long and Short Positions
- Concept 05: Quiz: Dtype
- Concept 06: Trading Strategy
- Concept 07: Quiz: Momentum-based Portfolio
- Concept 08: Quiz: Calculate Top and Bottom Performing
- Concept 09: Statistical Analysis
- Concept 10: The Many Meanings of “Alpha”
- Concept 11: Quiz: Test Returns for Statistical Significance
- Concept 12: Quiz: Statistical Analysis
- Concept 13: Finding Alpha
- Concept 14: Interlude: Global Talent
- Lesson 09: Project 1: Trading with MomentumLearn to implement a trading strategy on your own and test to see if it has the potential to be profitable.Project Description - Trading with MomentumProject Rubric - Trading with Momentum
- Module 02: Advanced Quants
- Lesson 01: Quant WorkflowLearn about the overall quant workflow, including alpha signal generation, alpha combination, portfolio optimization, and trading.
- Lesson 02: Outliers and FilteringLearn the importance of outliers and how to detect them. Learn about methods designed to handle outliers.
- Concept 01: Intro
- Concept 02: Sources of Outliers
- Concept 03: Outliers Due to Real Events
- Concept 04: Outliers, Signals and Strategies
- Concept 05: Spotting Outliers in Raw Data
- Concept 06: Handling Outliers in Raw Data
- Concept 07: Spotting Outliers in Signal Returns
- Concept 08: Handling Outliers in Signal Returns
- Concept 09: Generating Robust Trading Signals
- Concept 10: Summary
- Lesson 03: RegressionLearn about regression, and related statistical tools that pre-process data before regression analysis. See how regression relates to trading and other more advanced methods.
- Concept 01: Intro
- Concept 02: Distributions
- Concept 03: Exercise: Visualize Distributions
- Concept 04: Parameters of a Distribution
- Concept 05: Quiz: Standard Normal Distribution
- Concept 06: Testing for Normality
- Concept 07: Quiz: Normality
- Concept 08: Exercise: Normality
- Concept 09: Heteroskedasticity
- Concept 10: Transforming Data
- Concept 11: Linear Regression
- Concept 12: Breusch Pagan in Depth (Optional)
- Concept 13: Quiz: Regression
- Concept 14: Multivariate Linear Regression
- Concept 15: Regression in Trading
- Concept 16: Exercise: regression with two stocks
- Concept 17: Summary
- Concept 18: Interlude: Your Brain
- Lesson 04: Time Series ModelingLearn about advanced methods for time series analysis, including ARMA, ARIMA, Kalman Filters, Particle Filters, and recurrent neural networks.
- Lesson 05: VolatilityLearn about stock volatility, and how the GARCH model analysis volatility. See how volatility is used in equity trading.
- Concept 01: What is Volatility?
- Concept 02: Historical Volatility
- Concept 03: Annualized Volatility
- Concept 04: Scale of Volatility
- Concept 05: Quiz: Volatility
- Concept 06: Rolling Windows
- Concept 07: Quiz: Rolling Windows
- Concept 08: Exponentially Weighted Moving Average
- Concept 09: Quiz: Estimate Volatility
- Concept 10: Forecasting Volatility
- Concept 11: Markets & Volatility
- Concept 12: Using Volatility for Equity Trading
- Concept 13: Breakout Strategies
- Concept 14: Summary
- Lesson 06: Pairs Trading and Mean ReversionLearn about pairs trading, and study the tools used in identifying stock pairs and making trading decisions.
- Concept 01: Intro
- Concept 02: Mean Reversion
- Concept 03: Pairs Trading
- Concept 04: Finding Pairs to Trade
- Concept 05: Quiz: Identify Pairs to Trade
- Concept 06: Cointegration
- Concept 07: ADF and roots
- Concept 08: Clustering Stocks
- Concept 09: Trade Pairs of Stocks
- Concept 10: Exercise: finding pairs
- Concept 11: Variations of Pairs Trading and Mean Reversion Trading
- Concept 12: 3 or more stocks (optional)
- Concept 13: Details of Johansen Test (optional)
- Concept 14: Summary
- Lesson 07: Project 2: Breakout StrategyImplement the breakout strategy, find and remove outliers, and test to see if it can be a profitable strategy.Project Description - Breakout StrategyProject Rubric - Breakout Strategy
- Module 03: Funds, ETFs, Portfolio Optimization
- Lesson 01: Stocks, Indices, FundsGain an overview of stocks, indices and funds. Also learn how to construct an index.
- Concept 01: Intro Module 3
- Concept 02: Intro to this lesson
- Concept 03: Indices
- Concept 04: Market Cap
- Concept 05: Growth V. Value
- Concept 06: Ratios
- Concept 07: Index Categories
- Concept 08: Price Weighting
- Concept 09: Market Cap Weighting
- Concept 10: Adding or Removing from an Index
- Concept 11: How an Index is Constructed
- Concept 12: Hang Seng Index Construction
- Concept 13: Index after Add or Delete
- Concept 14: Funds
- Concept 15: Active vs. Passive
- Concept 16: Quiz: Rate of Returns Over Multiple Periods
- Concept 17: Smart Beta
- Concept 18: Mutual Funds
- Concept 19: Hedge Funds
- Concept 20: Relative and Absolute Returns
- Concept 21: Hedging Strategies
- Concept 22: Net Asset Value
- Concept 23: Expense Ratios
- Concept 24: Open End Mutual Funds
- Concept 25: Handling Withdrawals
- Concept 26: Close End Mutual Funds
- Concept 27: Transaction Costs
- Concept 28: Summary
- Lesson 02: ETFsLearn about Exchanged Traded Funds (ETFs) and how they are used by investors and fund managers.
- Concept 01: Intro
- Concept 02: Shortcomings of Mutual Funds
- Concept 03: How ETFs are Used
- Concept 04: Hedging
- Concept 05: ETF Sponsors
- Concept 06: Authorized Participant and the Create Process
- Concept 07: Redeeming Shares
- Concept 08: Lower Operational Costs & Taxes
- Concept 09: Arbitrage
- Concept 10: Arbitrage for Efficient ETF Pricing
- Concept 11: Summary
- Concept 12: Interlude: Meditation
- Lesson 03: Portfolio Risk and ReturnLearn the fundamentals of portfolio theory, which are key to designing portfolios for mutual funds, hedge funds and ETFs.
- Concept 01: Intro
- Concept 02: Diversification
- Concept 03: Portfolio Mean
- Concept 04: Portfolio Variance
- Concept 05: Reducing Risk
- Concept 06: Variance of a 3-Asset Portfolio
- Concept 07: The Covariance Matrix and Quadratic Forms
- Concept 08: Calculate a Covariance Matrix
- Concept 09: Quiz: np.cov
- Concept 10: The Efficient Frontier
- Concept 11: Capital Market Line
- Concept 12: The Sharpe Ratio
- Concept 13: Other Risk Measures
- Concept 14: The Capital Assets Pricing Model
- Concept 15: Quiz: Portfolio Return with a 3-Asset Portfolio
- Concept 16: Summary
- Lesson 04: Portfolio OptimizationLearn how to optimize portfolios to meet certain criteria and constraints. Get hands on experience in optimizing a portfolio with the cvxpy Python library.
- Concept 01: Intro
- Concept 02: What is Optimization?
- Concept 03: Optimization with Constraints
- Concept 04: Two-Asset Portfolio Optimization
- Concept 05: Portfolio Optimization with 2 Stocks
- Concept 06: Formulating Portfolio Optimization Problems
- Concept 07: cvxpy
- Concept 08: Exercise: cvxpy
- Concept 09: Exercise: cvxpy advanced optimization
- Concept 10: Rebalancing a Portfolio
- Concept 11: Rebalancing Strategies
- Concept 12: Limitations of the Classical Approach
- Concept 13: Summary
- Lesson 05: Project 3: Smart Beta and Portfolio OptimizationBuild a smart beta portfolio against an index and optimize a portfolio using quadratic programming.Project Description - Smart Beta and Portfolio OptimizationProject Rubric - Smart Beta and Portfolio Optimization
- Lesson 01: Stocks, Indices, FundsGain an overview of stocks, indices and funds. Also learn how to construct an index.
Module 04: Factor Investing and Alpha Research
- Lesson 01: FactorsIn the next 7 lessons and project, learn about factor investing and alpha research. These lessons and the project were designed by Jonathan Larkin, equities trader and quant investor.
- Concept 01: Intro to the Module
- Concept 02: Intro to the Lesson
- Concept 03: Example of a factor
- Concept 04: Quiz: factor values and weights
- Concept 05: Standardizing a factor
- Concept 06: De-mean part 1
- Concept 07: De-mean part 2
- Concept 08: Rescale part 1
- Concept 09: Rescale Part 2
- Concept 10: Overview for standardizing a factor
- Concept 11: Quiz: dollar neutral and leverage ratio
- Concept 12: Zipline Pipeline
- Concept 13: Zipline Coding Exercises
- Lesson 02: Factor Models and Types of FactorsLearn the theory of factor models, distinguish between alpha and risk factors, and get an overview of types of factors.
- Concept 01: Intro to Lesson
- Concept 02: What is a Factor Model?
- Concept 03: Factor Returns as Latent Variables
- Concept 04: Terminology
- Concept 05: Factor Model Assumptions
- Concept 06: Covariance Matrix Using a Factor Model
- Concept 07: Factor Models in Quant Finance
- Concept 08: Risk Factors v. Alpha Factors
- Concept 09: Risk Factors v. Alpha Factors part 2
- Concept 10: Risk Factors v. Alpha Factors part 3
- Concept 11: Risk Factors v. Alpha Factors part 4
- Concept 12: How an alpha factor becomes a risk factor part 1
- Concept 13: How an alpha factor becomes a risk factor part 2
- Concept 14: Momentum or Reversal
- Concept 15: Price-Volume Factors
- Concept 16: Volume Factors
- Concept 17: Fundamentals
- Concept 18: Fundamental Ratios
- Concept 19: Event-Driven Factors
- Concept 20: Index Changes
- Concept 21: Pre and Post Event
- Concept 22: Analyst Ratings
- Concept 23: Alternative Data
- Concept 24: Sentiment Analysis on News and Social Media
- Concept 25: NLP used to enhance Fundamental Analysis
- Concept 26: Other Alternative Data
- Concept 27: Summary
- Lesson 03: Risk Factor ModelsLearn how to model portfolio risk using factors.
- Concept 01: Intro
- Concept 02: install libraries
- Concept 03: Motivation for Risk Factor Models
- Concept 04: Historical Variance Exercise
- Concept 05: Factor Model of Asset Return
- Concept 06: Factor Model of Asset Return Exercise
- Concept 07: Factor Model of Portfolio Return
- Concept 08: Preview of Portfolio Variance Formula
- Concept 09: Factor Model of Portfolio Return Exercise
- Concept 10: Variance of one stock
- Concept 11: Taking constants out of Variance and Covariance (optional)
- Concept 12: Variance of 2 stocks part 1
- Concept 13: Variance of 2 stocks part 2
- Concept 14: Covariance Matrix of Assets Exercise
- Concept 15: Portfolio Variance using Factor Model
- Concept 16: Portfolio Variance Exercise
- Concept 17: Types of Risk Models
- Concept 18: Interlude
- Lesson 04: Time Series and Cross Sectional Risk ModelsLearn about two important types of risk models: time series and cross-sectional risk models.
- Concept 01: Time Series Model: Factor Variance
- Concept 02: Time Series Model: Factor Exposure
- Concept 03: Time Series Model: specific variance
- Concept 04: Time Series Risk Model
- Concept 05: Size
- Concept 06: SMB
- Concept 07: Value (HML)
- Concept 08: Fama French SMB and HML
- Concept 09: Fama French Risk Model
- Concept 10: Cross Sectional Model
- Concept 11: A different approach
- Concept 12: Categorical Factors
- Concept 13: Categorical Variable Estimation
- Concept 14: Cross Section: Specific Variance
- Concept 15: Fundamental Factors
- Concept 16: Summary
- Lesson 05: Risk Factor Models with PCALearn about Principle Component Analysis and how it’s used to build risk factor models.
- Concept 01: Statistical Risk Model
- Concept 02: Vectors Two Ways
- Concept 03: Refresh Linear Algebra
- Concept 04: Bases as Languages
- Concept 05: Translating Between Bases
- Concept 06: The Core Idea
- Concept 07: PCA Exercise
- Concept 08: Writing it Down: Part 1
- Concept 09: Writing it Down: Part 2
- Concept 10: Writing it Down: Part 3
- Concept 11: Writing it Down: Part 4
- Concept 12: The Principal Components
- Concept 13: Explained Variance
- Concept 14: PCA Toy Problem
- Concept 15: PCA Coding Exercise
- Concept 16: PCA as a Factor Model
- Concept 17: PCA as a Factor Model: Part 2
- Concept 18: PCA as a Factor Model Coding Exercise
- Concept 19: Outro
- Lesson 06: Alpha FactorsLearn about alpha generation and evaluation from a practitioner’s perspective.
- Concept 01: Intro: Efficient Market hypothesis and Arbitrage opportunities
- Concept 02: install libraries
- Concept 03: Alpha Factors versus Risk Factor Modeling
- Concept 04: Definition of key words
- Concept 05: Researching Alphas from Academic Papers
- Concept 06: Controlling for Risk within an Alpha Factor Part 1
- Concept 07: Controlling for Risk within an Alpha Factor Part 2
- Concept 08: Sector Neutral Exercise
- Concept 09: Ranking Part 1
- Concept 10: Ranking Part 2
- Concept 11: Ranking in Zipline
- Concept 12: Ranking exercise
- Concept 13: Z score
- Concept 14: z-score quiz
- Concept 15: z-score exercise
- Concept 16: Smoothing
- Concept 17: Smoothing Quiz 1
- Concept 18: Smoothing Exercise
- Concept 19: Factor Returns
- Concept 20: Factor returns quiz
- Concept 21: get_clean_factor_and_forward_returns
- Concept 22: Factor and forward returns exercise
- Concept 23: Universe construction rule
- Concept 24: Return Denominator, Leverage, and Factor Returns
- Concept 25: Making dollar neutral and leverage ratio equal to one
- Concept 26: Factor returns coding exercise
- Concept 27: Sharpe Ratio
- Concept 28: Sharpe Ratio Coding Exercise
- Concept 29: Halfway There!
- Concept 30: Ranked Information Coefficient (Rank IC) : Part 1
- Concept 31: Ranked Information Coefficient (Rank IC) : Part 2
- Concept 32: Quiz factor_information_coefficient
- Concept 33: Rank IC coding exercise
- Concept 34: The Fundamental Law of Active Management: Part 1
- Concept 35: The Fundamental Law of Active Management: Part 2
- Concept 36: Real World Constraints: Liquidity
- Concept 37: Real World Constraints: Transaction Costs
- Concept 38: Turnover as a Proxy for Real World Constraints
- Concept 39: Factor Rank Autocorrelation (Turnover)
- Concept 40: Turnover Exercise
- Concept 41: Quantile Analysis Part 1
- Concept 42: Quantile Analysis Part 2
- Concept 43: mean returns by quantile quiz
- Concept 44: Quantile analysis exercise
- Concept 45: Quantiles: Academic Research vs. Practitioners
- Concept 46: Transfer Coefficient
- Concept 47: Transfer Coefficient Coding Exercise
- Concept 48: It’s all Relative
- Concept 49: Conditional Factors
- Concept 50: Summary
- Concept 51: Interlude: Reading Academic Research Papers, Part 1
- Concept 52: Interlude: Reading Academic Research Papers, Part 2
- Concept 53: Interlude: Reading Academic Research Papers, Part 3
- Lesson 07: Alpha Factor Research MethodsLearn about alpha research from a practitioner’s perspective.
- Concept 01: Case Studies Intro
- Concept 02: install libraries
- Concept 03: Overnight Returns Abstract
- Concept 04: Overnight Returns Possible Alpha Factors
- Concept 05: Overnight Returns Data, Universe, Methods
- Concept 06: Overnight Returns: Methods: Quantile Analysis
- Concept 07: Overnight Returns exercise
- Concept 08: Winners and Losers in Momentum Investing
- Concept 09: Winners and Losers: Accelerated and Decelerated Gains and Losses
- Concept 10: Winners and Losers: approximating curves with polynomials
- Concept 11: Winners and Losers Content Quiz
- Concept 12: Winners and Losers: Creating a joint factor
- Concept 13: Winners and Losers in Momentum Exercise
- Concept 14: Skewness and Momentum: Attentional Bias
- Concept 15: Skewness and Momentum: Defining Skew
- Concept 16: Skewness and Momentum: Momentum Enhanced or weakened by Skew
- Concept 17: Skewness and Momentum: Conditional Factor
- Concept 18: iVol: Value and Idiosyncratic volatility Overview
- Concept 19: iVol: Arbitrage and Efficient Pricing of Stocks
- Concept 20: iVol: Arbitrage Risk
- Concept 21: iVol: Idiosyncratic Volatility
- Concept 22: iVol: Value, Fundamental or Discretionary Investing
- Concept 23: iVOL: Quantamental Investing
- Concept 24: iVol: Joint Factor: Volatility Enhanced Price Earnings Ratio
- Concept 25: iVol: Generalizing the volatility Factor
- Concept 26: Summary
- Concept 27: Interlude
- Lesson 08: Advanced Portfolio OptimizationLearn about portfolio optimization using alpha factors and risk factor models.
- Concept 01: Intro
- Concept 02: Setting Up the Problem: Alphas
- Concept 03: Setting Up the Problem: Risk
- Concept 04: Regularization
- Concept 05: Standard Constraints
- Concept 06: Leverage Constraint
- Concept 07: Factor Exposure and Position Constraints
- Concept 08: Advanced Optimization Exercise
- Concept 09: Alternative Ways of Setting Up the Problem
- Concept 10: Estimation Error
- Concept 11: Infeasible Problems
- Concept 12: Transaction Costs
- Concept 13: Will the Portfolios Be Different?
- Concept 14: Path Dependency
- Concept 15: What Is Optimization Doing to Our Alphas?
- Concept 16: Outro
- Concept 17: Interlude
- Concept 18: Feedback
- Lesson 09: Project 4: Alpha Research and Factor ModelingResearch and implement alpha factors, build a risk factor model. Use alpha factors and risk factors to optimize a portfolio.Project Description - Multi-factor ModelProject Rubric - Multi-factor Model
- Concept 01: Intro
- Concept 02: Project Description
- Concept 03: Project 4 Workspace
- Concept 04: Outro: What’s next
Part 02 : AI Algorithms in Trading
Learn how to analyze alternative data and use machine learning to generate trading signals. Run a backtest to evaluate and combine top performing signals.
- Lesson 01: FactorsIn the next 7 lessons and project, learn about factor investing and alpha research. These lessons and the project were designed by Jonathan Larkin, equities trader and quant investor.
Module 01: M5
- Lesson 01: Welcome To Term IIWelcome to Term 2! Say hello to your instructors and get an overview of the program.
- Lesson 02: Intro to Natural Language ProcessingLearn how to build a Natural Language Processing pipeline.
- Concept 01: NLP Overview
- Concept 02: Structured Languages
- Concept 03: Grammar
- Concept 04: Unstructured Text
- Concept 05: Counting Words
- Concept 06: Context Is Everything
- Concept 07: NLP and Pipelines
- Concept 08: How NLP Pipelines Work
- Concept 09: Text Processing
- Concept 10: Feature Extraction
- Concept 11: Modeling
- Lesson 03: Text ProcessingLearn to prepare text obtained from different sources for further processing, by cleaning, normalizing and splitting it into individual words or tokens.
- Concept 01: Text Processing
- Concept 02: Coding Examples
- Concept 03: Capturing Text Data
- Concept 04: Normalization
- Concept 05: Tokenization
- Concept 06: Cleaning
- Concept 07: Stop Word Removal
- Concept 08: Part-of-Speech Tagging
- Concept 09: Named Entity Recognition
- Concept 10: Stemming and Lemmatization
- Concept 11: Exercise: Process Tweets
- Concept 12: Text Processing Coding Examples
- Concept 13: Summary
- Lesson 04: Feature ExtractionTransform text using methods like Bag-of-Words, TF-IDF, Word2Vec and GloVE to extract features that you can use in machine learning models.
- Lesson 05: Financial StatementsLearn how to scrape data from financial documents using Regular Expressions and BeautifulSoup
- Concept 01: Introduction
- Concept 02: Financial Statements
- Concept 03: 10-K Walkthrough
- Concept 04: Quiz: 10-Ks and EDGAR
- Concept 05: Introduction to Regexes
- Concept 06: Raw Strings
- Concept 07: Finding Words
- Concept 08: Finding MetaCharacters
- Concept 09: Searching For Simple Patterns
- Concept 10: Word Boundaries
- Concept 11: Simple MetaCharacters
- Concept 12: Character Sets
- Concept 13: Groups
- Concept 14: Substitutions and Flags
- Concept 15: Applying Regexes to 10-Ks
- Concept 16: Introduction to BeautifulSoup
- Concept 17: Parsers
- Concept 18: HTML Structure
- Concept 19: Parsing an HTML File
- Concept 20: Navigating The Parse Tree
- Concept 21: Searching The Parse Tree
- Concept 22: Searching by Class and Regexes
- Concept 23: Children Tags
- Concept 24: Exercise: Get Headers and Paragraphs
- Concept 25: The Requests Library
- Concept 26: Summary
- Lesson 06: Basic NLP AnalysisLearn how to apply to NLP to financial statements
- Lesson 07: Project 5: NLP on Financial StatementsNLP Analysis on 10-k financial statements to generate an alpha factor.Project Description - NLP on Financial StatementsProject Rubric - NLP on Financial Statements
- Module 02: M6
- Lesson 01: Introduction to Neural NetworksIn this lesson, Luis will teach you the foundations of deep learning and neural networks. You’ll also implement gradient descent and backpropagation in python, right here in the classroom!
- Concept 01: Instructor
- Concept 02: Introduction
- Concept 03: Classification Problems 1
- Concept 04: Classification Problems 2
- Concept 05: Linear Boundaries
- Concept 06: Higher Dimensions
- Concept 07: Perceptrons
- Concept 08: Why “Neural Networks”?
- Concept 09: Perceptrons as Logical Operators
- Concept 10: Perceptron Trick
- Concept 11: Perceptron Algorithm
- Concept 12: Non-Linear Regions
- Concept 13: Error Functions
- Concept 14: Log-loss Error Function
- Concept 15: Discrete vs Continuous
- Concept 16: Softmax
- Concept 17: One-Hot Encoding
- Concept 18: Maximum Likelihood
- Concept 19: Maximizing Probabilities
- Concept 20: Cross-Entropy 1
- Concept 21: Cross-Entropy 2
- Concept 22: Multi-Class Cross Entropy
- Concept 23: Logistic Regression
- Concept 24: Gradient Descent
- Concept 25: Logistic Regression Algorithm
- Concept 26: Pre-Notebook: Gradient Descent
- Concept 27: Notebook: Gradient Descent
- Concept 28: Perceptron vs Gradient Descent
- Concept 29: Continuous Perceptrons
- Concept 30: Non-linear Data
- Concept 31: Non-Linear Models
- Concept 32: Neural Network Architecture
- Concept 33: Feedforward
- Concept 34: Backpropagation
- Concept 35: Pre-Notebook: Analyzing Student Data
- Concept 36: Notebook: Analyzing Student Data
- Concept 37: Outro
- Lesson 02: Training Neural NetworksNow that you know what neural networks are, in this lesson you will learn several techniques to improve their training.
- Concept 01: Instructor
- Concept 02: Training Optimization
- Concept 03: Testing
- Concept 04: Overfitting and Underfitting
- Concept 05: Early Stopping
- Concept 06: Regularization
- Concept 07: Regularization 2
- Concept 08: Dropout
- Concept 09: Local Minima
- Concept 10: Random Restart
- Concept 11: Vanishing Gradient
- Concept 12: Other Activation Functions
- Concept 13: Batch vs Stochastic Gradient Descent
- Concept 14: Learning Rate Decay
- Concept 15: Momentum
- Concept 16: Error Functions Around the World
- Lesson 03: Deep Learning with PyTorchLearn how to use PyTorch for building deep learning models
- Concept 01: Welcome!
- Concept 02: Pre-Notebook
- Concept 03: Notebook Workspace
- Concept 04: Single layer neural networks
- Concept 05: Single layer neural networks solution
- Concept 06: Networks Using Matrix Multiplication
- Concept 07: Multilayer Networks Solution
- Concept 08: Neural Networks in PyTorch
- Concept 09: Neural Networks Solution
- Concept 10: Implementing Softmax Solution
- Concept 11: Network Architectures in PyTorch
- Concept 12: Network Architectures Solution
- Concept 13: Training a Network Solution
- Concept 14: Classifying Fashion-MNIST
- Concept 15: Fashion-MNIST Solution
- Concept 16: Inference and Validation
- Concept 17: Validation Solution
- Concept 18: Dropout Solution
- Concept 19: Saving and Loading Models
- Concept 20: Loading Image Data
- Concept 21: Loading Image Data Solution
- Concept 22: Pre-Notebook with GPU
- Concept 23: Notebook Workspace w/ GPU
- Concept 24: Transfer Learning II
- Concept 25: Transfer Learning Solution
- Concept 26: Tips, Tricks, and Other Notes
- Lesson 04: Recurrent Neural NetworksLearn how to use recurrent neural networks to learn from sequential data such as text. Build a network that can generate realistic text one letter at a time.
- Concept 01: Intro to RNNs
- Concept 02: RNN vs LSTM
- Concept 03: Basics of LSTM
- Concept 04: Architecture of LSTM
- Concept 05: The Learn Gate
- Concept 06: The Forget Gate
- Concept 07: The Remember Gate
- Concept 08: The Use Gate
- Concept 09: Putting it All Together
- Concept 10: Other architectures
- Concept 11: Implementing RNNs
- Concept 12: Time-Series Prediction
- Concept 13: Training & Memory
- Concept 14: Character-wise RNNs
- Concept 15: Sequence Batching
- Concept 16: Notebook: Character-Level RNN
- Concept 17: Implementing a Char-RNN
- Concept 18: Batching Data, Solution
- Concept 19: Defining the Model
- Concept 20: Char-RNN, Solution
- Concept 21: Making Predictions
- Lesson 05: Embeddings & Word2VecIn this lesson, you’ll learn about embeddings in neural networks by implementing the Word2Vec model.
- Concept 01: Word Embeddings
- Concept 02: Embedding Weight Matrix/Lookup Table
- Concept 03: Word2Vec Notebook
- Concept 04: Pre-Notebook: Word2Vec, SkipGram
- Concept 05: Notebook: Word2Vec, SkipGram
- Concept 06: Data & Subsampling
- Concept 07: Subsampling Solution
- Concept 08: Context Word Targets
- Concept 09: Batching Data, Solution
- Concept 10: Word2Vec Model
- Concept 11: Model & Validations
- Concept 12: Negative Sampling
- Concept 13: Pre-Notebook: Negative Sampling
- Concept 14: Notebook: Negative Sampling
- Concept 15: SkipGramNeg, Model Definition
- Concept 16: Complete Model & Custom Loss
- Lesson 06: Sentiment Prediction RNNImplement a sentiment prediction RNN for predicting whether a movie review is positive or negative!
- Concept 01: Sentiment RNN, Introduction
- Concept 02: Pre-Notebook: Sentiment RNN
- Concept 03: Notebook: Sentiment RNN
- Concept 04: Data Pre-Processing
- Concept 05: Encoding Words, Solution
- Concept 06: Getting Rid of Zero-Length
- Concept 07: Cleaning & Padding Data
- Concept 08: Padded Features, Solution
- Concept 09: TensorDataset & Batching Data
- Concept 10: Defining the Model
- Concept 11: Complete Sentiment RNN
- Concept 12: Training the Model
- Concept 13: Testing
- Concept 14: Inference, Solution
- Lesson 07: Project 6: Sentiment Analysis with Neural NetworksBuild a deep learning model to classify the sentiment of messages.Project Description - Sentiment Analysis with Neural NetworksProject Rubric - Sentiment Analysis with Neural Networks
- Lesson 01: Introduction to Neural NetworksIn this lesson, Luis will teach you the foundations of deep learning and neural networks. You’ll also implement gradient descent and backpropagation in python, right here in the classroom!
- Module 03: M7
- Lesson 01: OverviewLearn about machine learning from a bird’s-eye-view.
- Lesson 02: Decision TreesDecision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.
- Concept 01: Welcome
- Concept 02: Intro
- Concept 03: Recommending Apps 1
- Concept 04: Recommending Apps 2
- Concept 05: Recommending Apps 3
- Concept 06: Tree Anatomy
- Concept 07: Quiz: Student Admissions
- Concept 08: Solution: Student Admissions
- Concept 09: Entropy
- Concept 10: Entropy Formula 1
- Concept 11: Entropy Formula 2
- Concept 12: Entropy Formula 3
- Concept 13: Quiz: Do You Know Your Entropy?
- Concept 14: Multiclass Entropy
- Concept 15: Quiz: Information Gain
- Concept 16: Solution: Information Gain
- Concept 17: Maximizing Information Gain
- Concept 18: Calculating Information Gain on a Dataset
- Concept 19: Gini Impurity
- Concept 20: Hyperparameters
- Concept 21: Decision Trees in sklearn
- Concept 22: Titanic Survival Model with Decision Trees
- Concept 23: [Solution] Titanic Survival Model
- Concept 24: Visualizing Your Tree
- Concept 25: Visualizing Your Tree Exercise
- Concept 26: Outro
- Lesson 03: Model Testing and EvaluationLearn about metrics to evaluate models and about how to avoid over- and underfitting.
- Concept 01: Intro
- Concept 02: Outline
- Concept 03: Testing your models
- Concept 04: Confusion Matrix
- Concept 05: Confusion Matrix 2
- Concept 06: Accuracy
- Concept 07: Accuracy 2
- Concept 08: When accuracy won’t work
- Concept 09: False Negatives and Positives
- Concept 10: Precision and Recall
- Concept 11: Precision
- Concept 12: Recall
- Concept 13: Types of Errors
- Concept 14: Model Complexity Graph
- Concept 15: Cross Validation
- Concept 16: K-Fold Cross Validation
- Concept 17: Cross Validation for Time Series
- Concept 18: Validation for Financial Data
- Concept 19: Learning Curves
- Concept 20: Detecting Overfitting and Underfitting with Learning Curves
- Concept 21: Solution: Detecting Overfitting and Underfitting
- Concept 22: Outro
- Lesson 04: Random ForestsLearn about random forest models and how to use them to combine alpha factors.
- Concept 01: Intro
- Concept 02: Review Decision Trees
- Concept 03: Ensemble Methods
- Concept 04: Perturbations on Columns
- Concept 05: Perturbations on Rows
- Concept 06: Forests of Randomized Trees
- Concept 07: Random Forests Exercise
- Concept 08: The Out-of-Bag Estimate
- Concept 09: Random Forest Hyperparameters
- Concept 10: Choosing Hyperparameter Values
- Concept 11: Random Forests for Alpha Combination
- Concept 12: Outro
- Lesson 05: Feature EngineeringLearn to engineer features such as market dispersion, market volatility, sector and date parts. Also learn to engineer targets (labels) that are robust to market changes over time.
- Lesson 06: Overlapping LabelsLearn about an issue with non-independent labels that comes up during alpha combination with machine learning models.
- Lesson 07: Feature ImportanceFeature importance helps us decide how relevant each feature is to a machine learning model’s predictions. Learn about two methods for calculating feature importance.
- Concept 01: Intro
- Concept 02: Feature Importance in Finance
- Concept 03: Feature Importance in Scikit-learn
- Concept 04: sklearn Exercise
- Concept 05: sklearn Code Walkthrough (Optional)
- Concept 06: When Feature Importance is Inconsistent
- Concept 07: Shapley Additive Explanations
- Concept 08: Shap Exercise
- Concept 09: Shapley Code Walkthrough (Optional)
- Concept 10: Tree Shap Exercise
- Concept 11: Tree Shap Code Walkthrough (Optional)
- Concept 12: Rank Features Exercise
- Concept 13: Ranking Features Walkthrough (optional)
- Concept 14: Outro
- Lesson 08: Project 7: Combining Signals for Enhanced AlphaBuild a random forest to generate better alpha.Project Description - Combining Signals for Enhanced AlphaProject Rubric - Combining Signals for Enhanced Alpha
- Module 04: Career Services
- Lesson 01: Take 30 Min to Improve your LinkedInFind your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.Project Description - Improve Your LinkedIn ProfileProject Rubric - Improve Your LinkedIn Profile
- Concept 01: Get Opportunities with LinkedIn
- Concept 02: Use Your Story to Stand Out
- Concept 03: Why Use an Elevator Pitch
- Concept 04: Create Your Elevator Pitch
- Concept 05: Use Your Elevator Pitch on LinkedIn
- Concept 06: Create Your Profile With SEO In Mind
- Concept 07: Profile Essentials
- Concept 08: Work Experiences & Accomplishments
- Concept 09: Build and Strengthen Your Network
- Concept 10: Reaching Out on LinkedIn
- Concept 11: Boost Your Visibility
- Concept 12: Up Next
- Lesson 02: Optimize Your GitHub ProfileOther professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.Project Description - Optimize Your GitHub ProfileProject Rubric - Optimize Your GitHub Profile
- Concept 01: Prove Your Skills With GitHub
- Concept 02: Introduction
- Concept 03: GitHub profile important items
- Concept 04: Good GitHub repository
- Concept 05: Interview with Art - Part 1
- Concept 06: Identify fixes for example “bad” profile
- Concept 07: Quick Fixes #1
- Concept 08: Quick Fixes #2
- Concept 09: Writing READMEs with Walter
- Concept 10: Interview with Art - Part 2
- Concept 11: Commit messages best practices
- Concept 12: Reflect on your commit messages
- Concept 13: Participating in open source projects
- Concept 14: Interview with Art - Part 3
- Concept 15: Participating in open source projects 2
- Concept 16: Starring interesting repositories
- Concept 17: Next Steps
- Lesson 01: Take 30 Min to Improve your LinkedInFind your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.Project Description - Improve Your LinkedIn ProfileProject Rubric - Improve Your LinkedIn Profile
Module 05: M8
- Lesson 01: Intro to BacktestingBacktesting helps you determine whether or not your strategies can be generalizable to future unseen data.
- Concept 01: Intro
- Concept 02: What is a Backtest?
- Concept 03: Backtest Validity
- Concept 04: Backtest Overfitting
- Concept 05: Overtrading
- Concept 06: Backtest Best Practices
- Concept 07: Structural Changes
- Concept 08: Gradient Boosting
- Concept 09: Overfitting Exercise
- Concept 10: AI in Finance Interview
- Concept 11: Outro
- Lesson 02: Optimization with Transaction CostsLearn about how to make the portfolio optimization in a backtest more realistic, and also more computationally efficient.
- Concept 01: intro
- Concept 02: exercise
- Concept 03: barra data
- Concept 04: time offsets
- Concept 05: holdings in dollars
- Concept 06: scaling alpha factor
- Concept 07: Transaction Costs
- Concept 08: Transaction cost formula
- Concept 09: Linear Transaction cost model
- Concept 10: Optimization without constraints
- Concept 11: Risk Factor Matrix
- Concept 12: Avoid N by N matrix
- Concept 13: risk aversion parameter
- Concept 14: objective function, gradient and optimizer
- Concept 15: outro
- Concept 16: ML for Trading interview
- Lesson 03: AttributionUse performance attribution to determine how each factor contributed to the portfolio’s results.
- Lesson 04: Project 8: BacktestingBuild a backtester using Barra data.Project Description - BacktestingProject Rubric - Backtesting
- Concept 01: Project Description
- Concept 02: Project 8 Workspace
- Concept 03: Congratulations!
Part 03 (Elective): Python Refresher
- Lesson 01: Intro to BacktestingBacktesting helps you determine whether or not your strategies can be generalizable to future unseen data.
Module 01: Elective Lessons
- Lesson 01: Why Python ProgrammingWelcome to Introduction to Python! Here’s an overview of the course.
- Lesson 02: Data Types and OperatorsFamiliarize yourself with the building blocks of Python! Learn about data types and operators, compound data structures, type conversion, built-in functions, and style guidelines.
- Concept 01: Introduction
- Concept 02: Arithmetic Operators
- Concept 03: Quiz: Arithmetic Operators
- Concept 04: Solution: Arithmetic Operators
- Concept 05: Variables and Assignment Operators
- Concept 06: Quiz: Variables and Assignment Operators
- Concept 07: Solution: Variables and Assignment Operators
- Concept 08: Integers and Floats
- Concept 09: Quiz: Integers and Floats
- Concept 10: Booleans, Comparison Operators, and Logical Operators
- Concept 11: Quiz: Booleans, Comparison Operators, and Logical Operators
- Concept 12: Solution: Booleans, Comparison and Logical Operators
- Concept 13: Strings
- Concept 14: Quiz: Strings
- Concept 15: Solution: Strings
- Concept 16: Type and Type Conversion
- Concept 17: Quiz: Type and Type Conversion
- Concept 18: Solution: Type and Type Conversion
- Concept 19: String Methods
- Concept 20: String Methods
- Concept 21: Another String Method - Split
- Concept 22: Lists and Membership Operators
- Concept 23: Quiz: Lists and Membership Operators
- Concept 24: Solution: List and Membership Operators
- Concept 25: List Methods
- Concept 26: Quiz: List Methods
- Concept 27: Tuples
- Concept 28: Quiz: Tuples
- Concept 29: Sets
- Concept 30: Quiz: Sets
- Concept 31: Dictionaries and Identity Operators
- Concept 32: Quiz: Dictionaries and Identity Operators
- Concept 33: Solution: Dictionaries and Identity Operators
- Concept 34: Quiz: More With Dictionaries
- Concept 35: Compound Data Structures
- Concept 36: Quiz: Compound Data Structures
- Concept 37: Solution: Compound Data Structions
- Concept 38: Conclusion
- Concept 39: Summary
- Lesson 03: Control FlowBuild logic into your code with control flow tools! Learn about conditional statements, repeating code with loops and useful built-in functions, and list comprehensions.
- Concept 01: Introduction
- Concept 02: Conditional Statements
- Concept 03: Practice: Conditional Statements
- Concept 04: Solution: Conditional Statements
- Concept 05: Quiz: Conditional Statements
- Concept 06: Solution: Conditional Statements
- Concept 07: Boolean Expressions for Conditions
- Concept 08: Quiz: Boolean Expressions for Conditions
- Concept 09: Solution: Boolean Expressions for Conditions
- Concept 10: For Loops
- Concept 11: Practice: For Loops
- Concept 12: Solution: For Loops Practice
- Concept 13: Quiz: For Loops
- Concept 14: Solution: For Loops Quiz
- Concept 15: Quiz: Match Inputs To Outputs
- Concept 16: Building Dictionaries
- Concept 17: Iterating Through Dictionaries with For Loops
- Concept 18: Quiz: Iterating Through Dictionaries
- Concept 19: Solution: Iterating Through Dictionaries
- Concept 20: While Loops
- Concept 21: Practice: While Loops
- Concept 22: Solution: While Loops Practice
- Concept 23: Quiz: While Loops
- Concept 24: Solution: While Loops Quiz
- Concept 25: Break, Continue
- Concept 26: Quiz: Break, Continue
- Concept 27: Solution: Break, Continue
- Concept 28: Zip and Enumerate
- Concept 29: Quiz: Zip and Enumerate
- Concept 30: Solution: Zip and Enumerate
- Concept 31: List Comprehensions
- Concept 32: Quiz: List Comprehensions
- Concept 33: Solution: List Comprehensions
- Concept 34: Conclusion
- Lesson 04: FunctionsLearn how to use functions to improve and reuse your code! Learn about functions, variable scope, documentation, lambda expressions, iterators, and generators.
- Concept 01: Introduction
- Concept 02: Defining Functions
- Concept 03: Quiz: Defining Functions
- Concept 04: Solution: Defining Functions
- Concept 05: Variable Scope
- Concept 06: Variable Scope
- Concept 07: Solution: Variable Scope
- Concept 08: Documentation
- Concept 09: Quiz: Documentation
- Concept 10: Solution: Documentation
- Concept 11: Lambda Expressions
- Concept 12: Quiz: Lambda Expressions
- Concept 13: Solution: Lambda Expressions
- Concept 14: [Optional] Iterators and Generators
- Concept 15: [Optional] Quiz: Iterators and Generators
- Concept 16: [Optional] Solution: Iterators and Generators
- Concept 17: [Optional] Generator Expressions
- Concept 18: Conclusion
- Concept 19: Further Learning
- Lesson 05: ScriptingSetup your own programming environment to write and run Python scripts locally! Learn good scripting practices, interact with different inputs, and discover awesome tools.
- Concept 01: Introduction
- Concept 02: Python Installation
- Concept 03: Install Python Using Anaconda
- Concept 04: [For Windows] Configuring Git Bash to Run Python
- Concept 05: Running a Python Script
- Concept 06: Programming Environment Setup
- Concept 07: Editing a Python Script
- Concept 08: Scripting with Raw Input
- Concept 09: Quiz: Scripting with Raw Input
- Concept 10: Solution: Scripting with Raw Input
- Concept 11: Errors and Exceptions
- Concept 12: Errors and Exceptions
- Concept 13: Handling Errors
- Concept 14: Practice: Handling Input Errors
- Concept 15: Solution: Handling Input Errors
- Concept 16: Accessing Error Messages
- Concept 17: Reading and Writing Files
- Concept 18: Quiz: Reading and Writing Files
- Concept 19: Solution: Reading and Writing Files
- Concept 20: Importing Local Scripts
- Concept 21: The Standard Library
- Concept 22: Quiz: The Standard Library
- Concept 23: Solution: The Standard Library
- Concept 24: Techniques for Importing Modules
- Concept 25: Quiz: Techniques for Importing Modules
- Concept 26: Third-Party Libraries
- Concept 27: Experimenting with an Interpreter
- Concept 28: Online Resources
- Concept 29: Conclusion
Part 04 (Elective): Linear Algebra
Module 01: Elective Lessons
- Lesson 01: IntroductionTake a sneak peek into the beautiful world of Linear Algebra and learn why it is such an important mathematical tool.
- Lesson 02: VectorsLearn about vectors, the basic building block of Linear Algebra.
- Concept 01: What’s a Vector?
- Concept 02: Vectors, what even are they? Part 2
- Concept 03: Vectors, what even are they? Part 3
- Concept 04: Vectors- Mathematical definition
- Concept 05: Transpose
- Concept 06: Magnitude and Direction
- Concept 07: Vectors- Quiz 1
- Concept 08: Operations in the Field
- Concept 09: Vector Addition
- Concept 10: Vectors- Quiz 2
- Concept 11: Scalar by Vector Multiplication
- Concept 12: Vectors Quiz 3
- Concept 13: Vectors Quiz Answers
- Lesson 03: Linear CombinationLearn how to scale and add vectors and how to visualize the process.
- Concept 01: Linear Combination. Part 1
- Concept 02: Linear Combination. Part 2
- Concept 03: Linear Combination and Span
- Concept 04: Linear Combination -Quiz 1
- Concept 05: Linear Dependency
- Concept 06: Solving a Simplified Set of Equations
- Concept 07: Linear Combination - Quiz 2
- Concept 08: Linear Combination - Quiz 3
- Lesson 04: Linear Transformation and Matrices What is a linear transformation and how is it directly related to matrices? Learn how to apply the math and visualize the concept.
- Concept 01: What is a Matrix?
- Concept 02: Matrix Addition
- Concept 03: Matrix Addition Quiz
- Concept 04: Scalar Multiplication of Matrix and Quiz
- Concept 05: Multiplication of Square Matrices
- Concept 06: Square Matrix Multiplication Quiz
- Concept 07: Matrix Multiplication - General
- Concept 08: Matrix Multiplication Quiz
- Concept 09: Linear Transformation and Matrices . Part 1
- Concept 10: Linear Transformation and Matrices. Part 2
- Concept 11: Linear Transformation and Matrices. Part 3
- Concept 12: Linear Transformation Quiz Answers
Part 05 (Elective): Jupyter Notebook, Numpy, and Pandas
Module 01: Elective Lessons
- Lesson 01: Jupyter NotebooksLearn how to use Jupyter Notebooks to create documents combining code, text, images, and more.
- Concept 01: Instructor
- Concept 02: What are Jupyter notebooks?
- Concept 03: Installing Jupyter Notebook
- Concept 04: Launching the notebook server
- Concept 05: Notebook interface
- Concept 06: Code cells
- Concept 07: Markdown cells
- Concept 08: Keyboard shortcuts
- Concept 09: Magic keywords
- Concept 10: Converting notebooks
- Concept 11: Creating a slideshow
- Concept 12: Finishing up
- Lesson 02: NumPyLearn the basics of NumPy and how to use it to create and manipulate arrays.
- Concept 01: Instructors
- Concept 02: Introduction to NumPy
- Concept 03: Why Use NumPy?
- Concept 04: Creating and Saving NumPy ndarrays
- Concept 05: Using Built-in Functions to Create ndarrays
- Concept 06: Create an ndarray
- Concept 07: Accessing, Deleting, and Inserting Elements Into ndarrays
- Concept 08: Slicing ndarrays
- Concept 09: Boolean Indexing, Set Operations, and Sorting
- Concept 10: Manipulating ndarrays
- Concept 11: Arithmetic operations and Broadcasting
- Concept 12: Creating ndarrays with Broadcasting
- Concept 13: Getting Set Up for the Mini-Project
- Concept 14: Mini-Project: Mean Normalization and Data Separation
- Lesson 03: PandasLearn the basics of Pandas Series and DataFrames and how to use them to load and process data.
- Concept 01: Instructors
- Concept 02: Introduction to pandas
- Concept 03: Why Use pandas?
- Concept 04: Creating pandas Series
- Concept 05: Accessing and Deleting Elements in pandas Series
- Concept 06: Arithmetic Operations on pandas Series
- Concept 07: Manipulate a Series
- Concept 08: Creating pandas DataFrames
- Concept 09: Accessing Elements in pandas DataFrames
- Concept 10: Dealing with NaN
- Concept 11: Manipulate a DataFrame
- Concept 12: Loading Data into a pandas DataFrame
- Concept 13: Getting Set Up for the Mini-Project
- Concept 14: Mini-Project: Statistics From Stock Data
Part 06 (Elective): Statistics
- Lesson 01: Jupyter NotebooksLearn how to use Jupyter Notebooks to create documents combining code, text, images, and more.
Module 01: Elective Lessons
- Lesson 01: Descriptive Statistics - Part IIn this lesson, you will learn about data types, measures of center, and the basics of statistical notation.
- Concept 01: Introduce Instructors
- Concept 02: Text: Optional Lessons Note
- Concept 03: Video: Welcome!
- Concept 04: Video: What is Data? Why is it important?
- Concept 05: Video: Data Types (Quantitative vs. Categorical)
- Concept 06: Quiz: Data Types (Quantitative vs. Categorical)
- Concept 07: Video: Data Types (Ordinal vs. Nominal)
- Concept 08: Video: Data Types (Continuous vs. Discrete)
- Concept 09: Video: Data Types Summary
- Concept 10: Text + Quiz: Data Types (Ordinal vs. Nominal)
- Concept 11: Data Types (Continuous vs. Discrete)
- Concept 12: Video: Introduction to Summary Statistics
- Concept 13: Video: Measures of Center (Mean)
- Concept 14: Measures of Center (Mean)
- Concept 15: Video: Measures of Center (Median)
- Concept 16: Measures of Center (Median)
- Concept 17: Video: Measures of Center (Mode)
- Concept 18: Measures of Center (Mode)
- Concept 19: Video: What is Notation?
- Concept 20: Video: Random Variables
- Concept 21: Quiz: Variable Types
- Concept 22: Video: Capital vs. Lower
- Concept 23: Quiz: Introduction to Notation
- Concept 24: Video: Better Way?
- Concept 25: Video: Summation
- Concept 26: Video: Notation for the Mean
- Concept 27: Quiz: Summation
- Concept 28: Quiz: Notation for the Mean
- Concept 29: Text: Summary on Notation
- Lesson 02: Descriptive Statistics - Part IIIn this lesson, you will learn about measures of spread, shape, and outliers as associated with quantitative data. You will also get a first look at inferential statistics.
- Concept 01: Video: What are Measures of Spread?
- Concept 02: Video: Histograms
- Concept 03: Video: Weekdays vs. Weekends: What is the Difference
- Concept 04: Video: Introduction to Five Number Summary
- Concept 05: Quiz: 5 Number Summary Practice
- Concept 06: Video: What if We Only Want One Number?
- Concept 07: Video: Introduction to Standard Deviation and Variance
- Concept 08: Video: Standard Deviation Calculation
- Concept 09: Measures of Spread (Calculation and Units)
- Concept 10: Text: Introduction to the Standard Deviation and Variance
- Concept 11: Video: Why the Standard Deviation?
- Concept 12: Video: Important Final Points
- Concept 13: Advanced: Standard Deviation and Variance
- Concept 14: Quiz: Applied Standard Deviation and Variance
- Concept 15: Homework 1: Final Quiz on Measures Spread
- Concept 16: Text: Measures of Center and Spread Summary
- Concept 17: Video: Shape
- Concept 18: Video: The Shape For Data In The World
- Concept 19: Quiz: Shape and Outliers (What’s the Impact?).html)
- Concept 20: Video: Shape and Outliers
- Concept 21: Video: Working With Outliers
- Concept 22: Video: Working With Outliers My Advice
- Concept 23: Quiz: Shape and Outliers (Comparing Distributions)
- Concept 24: Quiz: Shape and Outliers (Visuals)
- Concept 25: Quiz: Shape and Outliers (Final Quiz)
- Concept 26: Text: Descriptive Statistics Summary
- Concept 27: Video: Descriptive vs. Inferential Statistics
- Concept 28: Quiz: Descriptive vs. Inferential (Udacity Students)
- Concept 29: Quiz: Descriptive vs. Inferential (Bagels)
- Concept 30: Text: Descriptive vs. Inferential Summary
- Concept 31: Video: Summary
- Lesson 03: Admissions Case StudyLearn to ask the right questions, as you learn about Simpson’s Paradox.
- Concept 01: Admissions Case Study Introduction
- Concept 02: Admissions 1
- Concept 03: Admissions 2
- Concept 04: Admissions 3
- Concept 05: Admissions 4
- Concept 06: Gender Bias
- Concept 07: Aggregation
- Concept 08: Aggregation 2
- Concept 09: Aggregation 3
- Concept 10: Gender Bias Revisited
- Concept 11: Dangers of Statistics
- Concept 12: Text: Recap + Next Steps
- Concept 13: Case Study in Python
- Concept 14: Conclusion
- Lesson 04: ProbabilityGain the basics of probability using coins and die.
- Concept 01: Introduction to Probability
- Concept 02: Flipping Coins
- Concept 03: Fair Coin
- Concept 04: Loaded Coin 1
- Concept 05: Loaded Coin 2
- Concept 06: Loaded Coin 3
- Concept 07: Complementary Outcomes
- Concept 08: Two Flips 1
- Concept 09: Two Flips 2
- Concept 10: Two Flips 3
- Concept 11: Two Flips 4
- Concept 12: Two Flips 5
- Concept 13: One Head 1
- Concept 14: One Head 2
- Concept 15: One Of Three 1
- Concept 16: One Of Three 2
- Concept 17: Even Roll
- Concept 18: Doubles
- Concept 19: Probability Conclusion
- Concept 20: Text: Recap + Next Steps
- Lesson 05: Binomial DistributionLearn about one of the most popular distributions in probability - the Binomial Distribution.
- Concept 01: Binomial
- Concept 02: Heads Tails
- Concept 03: Heads Tails 2
- Concept 04: 5 Flips 1 Head
- Concept 05: 5 Flips 2 Heads
- Concept 06: 5 Flips 3 Heads
- Concept 07: 10 Flips 5 Heads
- Concept 08: Formula
- Concept 09: Arrangements
- Concept 10: Binomial 1
- Concept 11: Binomial 2
- Concept 12: Binomial 3
- Concept 13: Binomial 4
- Concept 14: Binomial 5
- Concept 15: Binomial 6
- Concept 16: Binomial Conclusion
- Concept 17: Text: Recap + Next Steps
- Lesson 06: Conditional ProbabilityNot all events are independent. Learn the probability rules for dependent events.
- Concept 01: Introduction to Conditional Probability
- Concept 02: Medical Example 1
- Concept 03: Medical Example 2
- Concept 04: Medical Example 3
- Concept 05: Medical Example 4
- Concept 06: Medical Example 5
- Concept 07: Medical Example 6
- Concept 08: Medical Example 7
- Concept 09: Medical Example 8
- Concept 10: Total Probability
- Concept 11: Two Coins 1
- Concept 12: Two Coins 2
- Concept 13: Two Coins 3
- Concept 14: Two Coins 4
- Concept 15: Summary
- Concept 16: Text: Summary
- Lesson 07: Bayes RuleLearn one of the most popular rules in all of statistics - Bayes rule.
- Concept 01: Bayes Rule
- Concept 02: Cancer Test
- Concept 03: Prior And Posterior
- Concept 04: Normalizing 1
- Concept 05: Normalizing 2
- Concept 06: Normalizing 3
- Concept 07: Total Probability
- Concept 08: Bayes Rule Diagram
- Concept 09: Equivalent Diagram
- Concept 10: Cancer Probabilities
- Concept 11: Probability Given Test
- Concept 12: Normalizer
- Concept 13: Normalizing Probability
- Concept 14: Disease Test 1
- Concept 15: Disease Test 2
- Concept 16: Disease Test 3
- Concept 17: Disease Test 4
- Concept 18: Disease Test 5
- Concept 19: Disease Test 6
- Concept 20: Bayes Rule Summary
- Concept 21: Robot Sensing 1
- Concept 22: Robot Sensing 2
- Concept 23: Robot Sensing 3
- Concept 24: Robot Sensing 4
- Concept 25: Robot Sensing 5
- Concept 26: Robot Sensing 6
- Concept 27: Robot Sensing 7
- Concept 28: Robot Sensing 8
- Concept 29: Generalizing
- Concept 30: Sebastian At Home
- Concept 31: Learning Objectives - Conditional Probability
- Concept 32: Reducing Uncertainty
- Concept 33: Bayes’ Rule and Robotics
- Concept 34: Learning from Sensor Data
- Concept 35: Using Sensor Data
- Concept 36: Learning Objectives - Bayes’ Rule
- Concept 37: Bayes Rule Conclusion
- Lesson 08: Python Probability PracticeTake what you have learned in the last lessons and put it to practice in Python.
- Lesson 09: Normal Distribution TheoryLearn the mathematics behind moving from a coin flip to a normal distribution.
- Concept 01: Maximum Probability
- Concept 02: Shape
- Concept 03: Better Formula
- Concept 04: Quadratics
- Concept 05: Quadratics 2
- Concept 06: Quadratics 3
- Concept 07: Quadratics 4
- Concept 08: Maximum
- Concept 09: Maximum Value
- Concept 10: Minimum
- Concept 11: Minimum Value
- Concept 12: Normalizer
- Concept 13: Formula Summary
- Concept 14: Central Limit Theorem
- Concept 15: Summary
- Lesson 10: Sampling distributions and the Central Limit TheoremLearn all about the underpinning of confidence intervals and hypothesis testing - sampling distributions.
- Concept 01: Introduction
- Concept 02: Video: Descriptive vs. Inferential Statistics
- Concept 03: Quiz: Descriptive vs. Inferential (Udacity Students)
- Concept 04: Quiz: Descriptive vs. Inferential (Bagels)
- Concept 05: Text: Descriptive vs. Inferential Statistics
- Concept 06: Video + Quiz: Introduction to Sampling Distributions Part I
- Concept 07: Video + Quiz: Introduction to Sampling Distributions Part II
- Concept 08: Video: Introduction to Sampling Distributions Part III
- Concept 09: Notebook + Quiz: Sampling Distributions & Python
- Concept 10: Text: Sampling Distribution Notes
- Concept 11: Video: Introduction to Notation
- Concept 12: Video: Notation for Parameters vs. Statistics
- Concept 13: Quiz: Notation
- Concept 14: Video: Other Sampling Distributions
- Concept 15: Video: Two Useful Theorems - Law of Large Numbers
- Concept 16: Notebook + Quiz: Law of Large Numbers
- Concept 17: Video: Two Useful Theorems - Central Limit Theorem
- Concept 18: Notebook + Quiz: Central Limit Theorem
- Concept 19: Notebook + Quiz: Central Limit Theorem - Part II
- Concept 20: Video: When Does the Central Limit Theorem Not Work?
- Concept 21: Notebook + Quiz: Central Limit Theorem - Part III
- Concept 22: Video: Bootstrapping
- Concept 23: Video: Bootstrapping & The Central Limit Theorem
- Concept 24: Notebook + Quiz: Bootstrapping
- Concept 25: Video: The Background of Bootstrapping
- Concept 26: Video: Why are Sampling Distributions Important
- Concept 27: Quiz + Text: Recap & Next Steps
- Lesson 11: Confidence IntervalsLearn how to use sampling distributions and bootstrapping to create a confidence interval for any parameter of interest.
- Concept 01: Video: Introduction
- Concept 02: Video: From Sampling Distributions to Confidence Intervals
- Concept 03: ScreenCast: Sampling Distributions and Confidence Intervals
- Concept 04: Notebook + Quiz: Building Confidence Intervals
- Concept 05: ScreenCast: Difference In Means
- Concept 06: Notebook + Quiz: Difference in Means
- Concept 07: Video: Confidence Interval Applications
- Concept 08: Video: Statistical vs. Practical Significance
- Concept 09: Statistical vs. Practical Significance
- Concept 10: Video: Traditional Confidence Intervals
- Concept 11: ScreenCast: Traditional Confidence Interval Methods
- Concept 12: Video: Other Language Associated with Confidence Intervals
- Concept 13: Other Language Associated with Confidence Intervals
- Concept 14: Video: Correct Interpretations of Confidence Intervals
- Concept 15: Correct Interpretations of Confidence Intervals
- Concept 16: Video: Confidence Intervals & Hypothesis Tests
- Concept 17: Text: Recap + Next Steps
- Lesson 12: Hypothesis TestingLearn the necessary skills to create and analyze the results in hypothesis testing.
- Concept 01: Introduction
- Concept 02: Hypothesis Testing
- Concept 03: Setting Up Hypothesis Tests - Part I
- Concept 04: Setting Up Hypotheses
- Concept 05: Setting Up Hypothesis Tests - Part II
- Concept 06: Quiz: Setting Up Hypothesis Tests
- Concept 07: Types of Errors - Part I
- Concept 08: Quiz: Types of Errors - Part I
- Concept 09: Types of Errors - Part II
- Concept 10: Quiz: Types of Errors - Part II(a)
- Concept 11: Quiz: Types of Errors - Part II(b)
- Concept 12: Types of Errors - Part III
- Concept 13: Quiz: Types of Errors - Part III
- Concept 14: Common Types of Hypothesis Tests
- Concept 15: Quiz: More Hypothesis Testing Practice
- Concept 16: How Do We Choose Between Hypotheses?
- Concept 17: Video: Simulating from the Null
- Concept 18: Notebook + Quiz: Simulating from the Null
- Concept 19: Solution Notebook: Simulating from the Null
- Concept 20: What is a p-value Anyway?
- Concept 21: Video: Calculating the p-value
- Concept 22: Quiz: What is a p-value Anyway?
- Concept 23: Quiz: Calculating a p-value
- Concept 24: Quiz: Calculating another p-value
- Concept 25: Connecting Errors and P-Values
- Concept 26: Conclusions in Hypothesis Testing
- Concept 27: Quiz: Connecting Errors and P-Values
- Concept 28: Notebook + Quiz: Drawing Conclusions
- Concept 29: Solution Notebook: Drawing Conclusions
- Concept 30: Other Things to Consider - Impact of Large Sample Size
- Concept 31: Other Things to Consider - What If We Test More Than Once?
- Concept 32: Other Things to Consider - How Do CIs and HTs Compare?
- Concept 33: Notebook + Quiz: Impact of Sample Size
- Concept 34: Solution Notebook: Impact of Sample Size
- Concept 35: Notebook + Quiz: Multiple Tests
- Concept 36: Solution Notebook: Multiple tests
- Concept 37: Hypothesis Testing Conclusion
- Concept 38: Quiz + Text: Recap
- Lesson 13: Case Study: A/B testsWork through a case study of how A/B testing works for an online education company called Audacity.
- Concept 01: Introduction
- Concept 02: A/B Testing
- Concept 03: A/B Testing
- Concept 04: Business Example
- Concept 05: Experiment I
- Concept 06: Quiz: Experiment I
- Concept 07: Metric - Click Through Rate
- Concept 08: Click Through Rate
- Concept 09: Experiment II
- Concept 10: Metric - Enrollment Rate
- Concept 11: Metric - Average Reading Duration
- Concept 12: Metric - Average Classroom Time
- Concept 13: Metric - Completion Rate
- Concept 14: Analyzing Multiple Metrics
- Concept 15: Quiz: Analyzing Multiple Metrics
- Concept 16: Drawing Conclusions
- Concept 17: Quiz: Difficulties in A/B Testing
- Concept 18: Conclusion
Part 07 (Elective): Machine Learning
- Lesson 01: Descriptive Statistics - Part IIn this lesson, you will learn about data types, measures of center, and the basics of statistical notation.
Module 01: Elective Lessons
- Lesson 01: Linear RegressionLinear regression is a very effective algorithm to predict numerical data.
- Concept 01: Intro
- Concept 02: Quiz: Housing Prices
- Concept 03: Solution: Housing Prices
- Concept 04: Fitting a Line Through Data
- Concept 05: Moving a Line
- Concept 06: Absolute Trick
- Concept 07: Square Trick
- Concept 08: Gradient Descent
- Concept 09: Mean Absolute Error
- Concept 10: Mean Squared Error
- Concept 11: Minimizing Error Functions
- Concept 12: Mean vs Total Error
- Concept 13: Mini-batch Gradient Descent
- Concept 14: Absolute Error vs Squared Error
- Concept 15: Linear Regression in scikit-learn
- Concept 16: Higher Dimensions
- Concept 17: Multiple Linear Regression
- Concept 18: Closed Form Solution
- Concept 19: (Optional) Closed form Solution Math
- Concept 20: Linear Regression Warnings
- Concept 21: Polynomial Regression
- Concept 22: Regularization
- Concept 23: Outro
- Lesson 02: Naive BayesNaive Bayesian Algorithms are powerful tools for creating classifiers for incoming labeled data.
- Concept 01: Intro
- Concept 02: Guess the Person
- Concept 03: Known and Inferred
- Concept 04: Guess the Person Now
- Concept 05: Bayes Theorem
- Concept 06: Quiz: False Positives
- Concept 07: Solution: False Positives
- Concept 08: Bayesian Learning 1
- Concept 09: Bayesian Learning 2
- Concept 10: Bayesian Learning 3
- Concept 11: Naive Bayes Algorithm 1
- Concept 12: Naive Bayes Algorithm 2
- Concept 13: Building a Spam Classifier
- Concept 14: Project
- Concept 15: Spam Classifier - Workspace
- Concept 16: Outro
- Lesson 03: ClusteringClustering is one of the most common methods of unsupervised learning. Here, we’ll discuss the K-means clustering algorithm.
- Concept 01: Introduction
- Concept 02: Unsupervised Learning
- Concept 03: Clustering Movies
- Concept 04: How Many Clusters?
- Concept 05: Match Points with Clusters
- Concept 06: Optimizing Centers (Rubber Bands)
- Concept 07: Moving Centers 2
- Concept 08: Match Points (again)
- Concept 09: Handoff to Katie
- Concept 10: K-Means Cluster Visualization
- Concept 11: K-Means Clustering Visualization 2
- Concept 12: K-Means Clustering Visualization 3
- Concept 13: Sklearn
- Concept 14: Some challenges of k-means
- Concept 15: Limitations of K-Means
- Concept 16: Counterintuitive Clusters
- Concept 17: Counterintuitive Clusters 2
- Lesson 04: Decision TreesDecision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.
- Concept 01: Intro
- Concept 02: Recommending Apps 1
- Concept 03: Recommending Apps 2
- Concept 04: Recommending Apps 3
- Concept 05: Quiz: Student Admissions
- Concept 06: Solution: Student Admissions
- Concept 07: Entropy
- Concept 08: Entropy Formula 1
- Concept 09: Entropy Formula 2
- Concept 10: Entropy Formula 3
- Concept 11: Multiclass Entropy
- Concept 12: Quiz: Information Gain
- Concept 13: Solution: Information Gain
- Concept 14: Maximizing Information Gain
- Concept 15: Random Forests
- Concept 16: Hyperparameters
- Concept 17: Decision Trees in sklearn
- Concept 18: Titanic Survival Model with Decision Trees
- Concept 19: [Solution] Titanic Survival Model
- Concept 20: Outro
- Lesson 05: Introduction to Kalman FiltersLearn the intuition behind the Kalman Filter, a vehicle tracking algorithm, and implement a one-dimensional tracker of your own.
- Concept 01: Kalman Filters and Linear Algebra
- Concept 02: Introduction
- Concept 03: Tracking Intro
- Concept 04: Answer: Tracking Intro
- Concept 05: Gaussian Intro
- Concept 06: Answer: Gaussian Intro
- Concept 07: Quiz: Variance and Preferred Gaussian
- Concept 08: Answer: Variance and Preferred Gaussian
- Concept 09: Gaussian Function and Maximum
- Concept 10: Quiz: Shifting the Mean
- Concept 11: Answer: Shifting the Mean
- Concept 12: Quiz: Predicting the Peak
- Concept 13: Answer: Predicting the Peak
- Concept 14: Quiz: Parameter Update
- Concept 15: Answer: Parameter Update
- Concept 16: Notebook: New Mean and Variance
- Concept 17: Solution: New Mean and Variance
- Concept 18: Quiz: Gaussian Motion
- Concept 19: Answer: Gaussian Motion
- Concept 20: Predict Function
- Concept 21: Notebook: Predict Function
- Concept 22: Answer: Predict Function
- Concept 23: Kalman Filter Code
- Concept 24: Notebook: 1D Kalman Filter
- Concept 25: Answer: 1D Kalman Filter
- Concept 26: Kalman Prediction
- Concept 27: Next: Motion Models and State
Part 08 (Elective): Deep Learning
- Lesson 01: Linear RegressionLinear regression is a very effective algorithm to predict numerical data.
Module 01: Elective Lessons
- Lesson 01: Introduction to Neural NetworksIn this lesson, Luis will teach you the foundations of deep learning and neural networks. You’ll also implement gradient descent and backpropagation in python, right here in the classroom!
- Concept 01: Instructor
- Concept 02: Introduction
- Concept 03: Classification Problems 1
- Concept 04: Classification Problems 2
- Concept 05: Linear Boundaries
- Concept 06: Higher Dimensions
- Concept 07: Perceptrons
- Concept 08: Why “Neural Networks”?
- Concept 09: Perceptrons as Logical Operators
- Concept 10: Perceptron Trick
- Concept 11: Perceptron Algorithm
- Concept 12: Non-Linear Regions
- Concept 13: Error Functions
- Concept 14: Log-loss Error Function
- Concept 15: Discrete vs Continuous
- Concept 16: Softmax
- Concept 17: One-Hot Encoding
- Concept 18: Maximum Likelihood
- Concept 19: Maximizing Probabilities
- Concept 20: Cross-Entropy 1
- Concept 21: Cross-Entropy 2
- Concept 22: Multi-Class Cross Entropy
- Concept 23: Logistic Regression
- Concept 24: Gradient Descent
- Concept 25: Logistic Regression Algorithm
- Concept 26: Pre-Notebook: Gradient Descent
- Concept 27: Notebook: Gradient Descent
- Concept 28: Perceptron vs Gradient Descent
- Concept 29: Continuous Perceptrons
- Concept 30: Non-linear Data
- Concept 31: Non-Linear Models
- Concept 32: Neural Network Architecture
- Concept 33: Feedforward
- Concept 34: Backpropagation
- Concept 35: Pre-Notebook: Analyzing Student Data
- Concept 36: Notebook: Analyzing Student Data
- Concept 37: Outro
Part 09 (Elective): Computer Vision
- Lesson 01: Introduction to Neural NetworksIn this lesson, Luis will teach you the foundations of deep learning and neural networks. You’ll also implement gradient descent and backpropagation in python, right here in the classroom!
Module 01: Elective Lessons
- Lesson 01: Intro to Computer VisionLearn what computer vision is all about, its applications in the field of artificial and emotional intelligence.
- Concept 01: Welcome to Computer Vision
- Concept 02: What is Vision?
- Concept 03: Role in AI
- Concept 04: Computer Vision Applications
- Concept 05: Emotional Intelligence
- Concept 06: Vision-based Emotion AI
- Concept 07: Computer Vision Pipeline
- Concept 08: Quiz: Pipeline Steps
- Concept 09: Training a Model
- Concept 10: AffdexMe Demo
- Concept 11: Emotion as a Service
- Concept 12: [Preview] Project: Mimic Me!
Part 10 (Elective): Natural Language Processing
- Lesson 01: Intro to Computer VisionLearn what computer vision is all about, its applications in the field of artificial and emotional intelligence.
Module 01: Elective Lessons
- Lesson 01: Intro to NLPArpan will give you an overview of how to build a Natural Language Processing pipeline.
- Concept 01: Introducing Arpan
- Concept 02: NLP Overview
- Concept 03: Structured Languages
- Concept 04: Grammar
- Concept 05: Unstructured Text
- Concept 06: Counting Words
- Concept 07: Context Is Everything
- Concept 08: NLP and Pipelines
- Concept 09: How NLP Pipelines Work
- Concept 10: Text Processing
- Concept 11: Feature Extraction
- Concept 12: Modeling
- Lesson 01: Intro to NLPArpan will give you an overview of how to build a Natural Language Processing pipeline.
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