Nanodegree key: nd880
Version: 5.0.0
Locale: enus
Complete realworld projects designed by industry experts, covering topics from asset management to trading signal generation. Master AI algorithms for trading, and build your careerready 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 multifactor model with optimization.

Module 01:
Quant Basics

Lesson 01: Welcome to the Nanodegree Program
Welcome to the exciting world of Quantitative Trading! Say hello to your instructors and get an overview of the program.

Lesson 02: Knowledge, Community, and Careers
You 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 Account
What to do if you have questions about your account or general questions about the program.

Lesson 04: Stock Prices
Learn about stocks and common terminology used when analyzing stocks.

Lesson 05: Market Mechanics
Learn 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 Processing
Learn 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 Returns
Learn how to calculate stock returns, and log returns in particular. Learn why log returns are used to analyze financial data.

Lesson 08: Momentum Trading
Learn 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: Momentumbased Signals
 Concept 03: Quiz: Momentumbased Signals
 Concept 04: Long and Short Positions
 Concept 05: Quiz: Dtype
 Concept 06: Trading Strategy
 Concept 07: Quiz: Momentumbased 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 Momentum
Learn to implement a trading strategy on your own and test to see if it has the potential to be profitable.


Module 02:
Advanced Quants

Lesson 01: Quant Workflow
Learn about the overall quant workflow, including alpha signal generation, alpha combination, portfolio optimization, and trading.

Lesson 02: Outliers and Filtering
Learn 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: Regression
Learn about regression, and related statistical tools that preprocess 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 Modeling
Learn about advanced methods for time series analysis, including ARMA, ARIMA, Kalman Filters, Particle Filters, and recurrent neural networks.

Lesson 05: Volatility
Learn 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 Reversion
Learn 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 Strategy
Implement the breakout strategy, find and remove outliers, and test to see if it can be a profitable strategy.


Module 03:
Funds, ETFs, Portfolio Optimization

Lesson 01: Stocks, Indices, Funds
Gain 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: ETFs
Learn 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 Return
Learn 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 3Asset 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 3Asset Portfolio
 Concept 16: Summary

Lesson 04: Portfolio Optimization
Learn 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: TwoAsset 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 Optimization
Build a smart beta portfolio against an index and optimize a portfolio using quadratic programming.


Module 04:
Factor Investing and Alpha Research

Lesson 01: Factors
In 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: Demean part 1
 Concept 07: Demean 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 Factors
Learn 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: PriceVolume Factors
 Concept 16: Volume Factors
 Concept 17: Fundamentals
 Concept 18: Fundamental Ratios
 Concept 19: EventDriven 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 Models
Learn 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 Models
Learn about two important types of risk models: time series and crosssectional 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 PCA
Learn 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 Factors
Learn 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: zscore quiz
 Concept 15: zscore 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 Methods
Learn 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 Optimization
Learn 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 Modeling
Research and implement alpha factors, build a risk factor model. Use alpha factors and risk factors to optimize a portfolio.

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.

Module 01:
M5 Sentiment Analysis with Natural Language Processing

Lesson 01: Welcome To Term II
Welcome to Term 2! Say hello to your instructors and get an overview of the program.

Lesson 02: Intro to Natural Language Processing
Learn 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 Processing
Learn 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: PartofSpeech 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 Extraction
Transform text using methods like BagofWords, TFIDF, Word2Vec and GloVE to extract features that you can use in machine learning models.

Lesson 05: Financial Statements
Learn how to scrape data from financial documents using Regular Expressions and BeautifulSoup
 Concept 01: Introduction
 Concept 02: Financial Statements
 Concept 03: 10K Walkthrough
 Concept 04: Quiz: 10Ks 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 10Ks
 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 Analysis
Learn how to apply to NLP to financial statements

Lesson 07: Project 5: NLP on Financial Statements
NLP Analysis on 10k financial statements to generate an alpha factor.


Module 02:
M6 Advanced Natural Language Processing with Deep Learning

Lesson 01: Introduction to Neural Networks
In 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: NonLinear Regions
 Concept 13: Error Functions
 Concept 14: Logloss Error Function
 Concept 15: Discrete vs Continuous
 Concept 16: Softmax
 Concept 17: OneHot Encoding
 Concept 18: Maximum Likelihood
 Concept 19: Maximizing Probabilities
 Concept 20: CrossEntropy 1
 Concept 21: CrossEntropy 2
 Concept 22: MultiClass Cross Entropy
 Concept 23: Logistic Regression
 Concept 24: Gradient Descent
 Concept 25: Logistic Regression Algorithm
 Concept 26: PreNotebook: Gradient Descent
 Concept 27: Notebook: Gradient Descent
 Concept 28: Perceptron vs Gradient Descent
 Concept 29: Continuous Perceptrons
 Concept 30: Nonlinear Data
 Concept 31: NonLinear Models
 Concept 32: Neural Network Architecture
 Concept 33: Feedforward
 Concept 34: Backpropagation
 Concept 35: PreNotebook: Analyzing Student Data
 Concept 36: Notebook: Analyzing Student Data
 Concept 37: Outro

Lesson 02: Training Neural Networks
Now 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 PyTorch
Learn how to use PyTorch for building deep learning models
 Concept 01: Welcome!
 Concept 02: PreNotebook
 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 FashionMNIST
 Concept 15: FashionMNIST 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: PreNotebook 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 Networks
Learn 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: TimeSeries Prediction
 Concept 13: Training & Memory
 Concept 14: Characterwise RNNs
 Concept 15: Sequence Batching
 Concept 16: Notebook: CharacterLevel RNN
 Concept 17: Implementing a CharRNN
 Concept 18: Batching Data, Solution
 Concept 19: Defining the Model
 Concept 20: CharRNN, Solution
 Concept 21: Making Predictions

Lesson 05: Embeddings & Word2Vec
In 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: PreNotebook: 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: PreNotebook: Negative Sampling
 Concept 14: Notebook: Negative Sampling
 Concept 15: SkipGramNeg, Model Definition
 Concept 16: Complete Model & Custom Loss

Lesson 06: Sentiment Prediction RNN
Implement a sentiment prediction RNN for predicting whether a movie review is positive or negative!
 Concept 01: Sentiment RNN, Introduction
 Concept 02: PreNotebook: Sentiment RNN
 Concept 03: Notebook: Sentiment RNN
 Concept 04: Data PreProcessing
 Concept 05: Encoding Words, Solution
 Concept 06: Getting Rid of ZeroLength
 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 Networks
Build a deep learning model to classify the sentiment of messages.
Project Description  Sentiment Analysis with Neural Networks


Module 03:
M7 Combining Multiple Signals

Lesson 01: Overview
Learn about machine learning from a bird'seyeview.

Lesson 02: Decision Trees
Decision trees are a structure for decisionmaking 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 Evaluation
Learn 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: KFold 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 Forests
Learn 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 OutofBag Estimate
 Concept 09: Random Forest Hyperparameters
 Concept 10: Choosing Hyperparameter Values
 Concept 11: Random Forests for Alpha Combination
 Concept 12: Outro

Lesson 05: Feature Engineering
Learn 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.
 Concept 01: Intro
 Concept 02: Review Random Forests
 Concept 03: Setup Code Exercise
 Concept 04: Feature Eng Exercise
 Concept 05: Universal Quant Features
 Concept 06: Market Dispersion
 Concept 07: Market Volatility
 Concept 08: Sector
 Concept 09: Date Parts
 Concept 10: Targets (Labels)
 Concept 11: Outro

Lesson 06: Overlapping Labels
Learn about an issue with nonindependent labels that comes up during alpha combination with machine learning models.

Lesson 07: Feature Importance
Feature 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 Scikitlearn
 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


Module 04:
Career Services

Lesson 01: Take 30 Min to Improve your LinkedIn
Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.
 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 Profile
Other professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.
 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


Module 05:
M8 Simulating Trades with Historical Data

Lesson 01: Intro to Backtesting
Backtesting 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 Costs
Learn 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: Attribution
Use performance attribution to determine how each factor contributed to the portfolio's results.
 Concept 01: Intro
 Concept 02: Review MultiFactor Models
 Concept 03: Exposure Vector
 Concept 04: Variance Decomposition
 Concept 05: Performance Attribution
 Concept 06: Performance Attribution Exercise
 Concept 07: Attribution Reporting
 Concept 08: Understanding Portfolio Characteristics
 Concept 09: Outro

Part 03 (Elective) : Python Refresher

Module 01:
Elective Lessons

Lesson 01: Why Python Programming
Welcome to Introduction to Python! Here's an overview of the course.

Lesson 02: Data Types and Operators
Familiarize yourself with the building blocks of Python! Learn about data types and operators, compound data structures, type conversion, builtin 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 Flow
Build logic into your code with control flow tools! Learn about conditional statements, repeating code with loops and useful builtin 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: Functions
Learn 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: Scripting
Setup 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: ThirdParty 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: Introduction
Take a sneak peek into the beautiful world of Linear Algebra and learn why it is such an important mathematical tool.

Lesson 02: Vectors
Learn 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 Combination
Learn 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 Notebooks
Learn 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: NumPy
Learn 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 Builtin 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 MiniProject
 Concept 14: MiniProject: Mean Normalization and Data Separation

Lesson 03: Pandas
Learn 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 MiniProject
 Concept 14: MiniProject: Statistics From Stock Data

Part 06 (Elective) : Statistics

Module 01:
Elective Lessons

Lesson 01: Descriptive Statistics  Part I
In 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 II
In 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?)
 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 Study
Learn 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: Probability
Gain 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 Distribution
Learn 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 Probability
Not 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 Rule
Learn 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 Practice
Take what you have learned in the last lessons and put it to practice in Python.

Lesson 09: Normal Distribution Theory
Learn 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 Theorem
Learn 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 Intervals
Learn 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 Testing
Learn 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 pvalue Anyway?
 Concept 21: Video: Calculating the pvalue
 Concept 22: Quiz: What is a pvalue Anyway?
 Concept 23: Quiz: Calculating a pvalue
 Concept 24: Quiz: Calculating another pvalue
 Concept 25: Connecting Errors and PValues
 Concept 26: Conclusions in Hypothesis Testing
 Concept 27: Quiz: Connecting Errors and PValues
 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 tests
Work 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

Module 01:
Elective Lessons

Lesson 01: Linear Regression
Linear 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: Minibatch Gradient Descent
 Concept 14: Absolute Error vs Squared Error
 Concept 15: Linear Regression in scikitlearn
 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 Bayes
Naive 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: Clustering
Clustering is one of the most common methods of unsupervised learning. Here, we'll discuss the Kmeans 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: KMeans Cluster Visualization
 Concept 11: KMeans Clustering Visualization 2
 Concept 12: KMeans Clustering Visualization 3
 Concept 13: Sklearn
 Concept 14: Some challenges of kmeans
 Concept 15: Limitations of KMeans
 Concept 16: Counterintuitive Clusters
 Concept 17: Counterintuitive Clusters 2

Lesson 04: Decision Trees
Decision trees are a structure for decisionmaking 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 Filters
Learn the intuition behind the Kalman Filter, a vehicle tracking algorithm, and implement a onedimensional 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

Module 01:
Elective Lessons

Lesson 01: Introduction to Neural Networks
In 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: NonLinear Regions
 Concept 13: Error Functions
 Concept 14: Logloss Error Function
 Concept 15: Discrete vs Continuous
 Concept 16: Softmax
 Concept 17: OneHot Encoding
 Concept 18: Maximum Likelihood
 Concept 19: Maximizing Probabilities
 Concept 20: CrossEntropy 1
 Concept 21: CrossEntropy 2
 Concept 22: MultiClass Cross Entropy
 Concept 23: Logistic Regression
 Concept 24: Gradient Descent
 Concept 25: Logistic Regression Algorithm
 Concept 26: PreNotebook: Gradient Descent
 Concept 27: Notebook: Gradient Descent
 Concept 28: Perceptron vs Gradient Descent
 Concept 29: Continuous Perceptrons
 Concept 30: Nonlinear Data
 Concept 31: NonLinear Models
 Concept 32: Neural Network Architecture
 Concept 33: Feedforward
 Concept 34: Backpropagation
 Concept 35: PreNotebook: Analyzing Student Data
 Concept 36: Notebook: Analyzing Student Data
 Concept 37: Outro

Part 09 (Elective) : Computer Vision

Module 01:
Elective Lessons

Lesson 01: Intro to Computer Vision
Learn 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: Visionbased 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

Module 01:
Elective Lessons

Lesson 01: Intro to NLP
Arpan 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
