Section 1 : Welcome
Lecture 1 Introduction and Outline 00:06:43 Duration
Lecture 2 Where to get the code 00:08:14 Duration
Lecture 3 Scope of the course 00:03:19 Duration
Lecture 4 How to Practice 00:03:35 Duration
Lecture 5 Warmup (Optional) 00:03:59 Duration

Section 2 : Financial Basics
Lecture 1 Financial Basics Section Introduction 00:05:23 Duration
Lecture 2 Getting Financial Data 00:07:11 Duration
Lecture 3 Getting Financial Data (Code) 00:07:06 Duration
Lecture 4 Understanding Financial Data 00:04:55 Duration
Lecture 5 Understanding Financial Data (Code) 00:11:58 Duration
Lecture 6 Dealing with Missing Data 00:05:48 Duration
Lecture 7 Dealing with Missing Data (Code) 00:06:51 Duration
Lecture 8 Returns 00:09:06 Duration
Lecture 9 Adjusted Close, Stock Splits, and Dividends 00:11:20 Duration
Lecture 10 Adjusted Close (Code) 00:03:39 Duration
Lecture 11 Back to Returns (Code)
Lecture 12 QQ-Plots
Lecture 13 QQ-Plots (Code) 00:07:09 Duration
Lecture 14 The t-Distribution 00:03:45 Duration
Lecture 15 The t-Distribution (Code) 00:07:58 Duration
Lecture 16 Skewness and Kurtosis 00:07:25 Duration
Lecture 17 Confidence Intervals 00:10:19 Duration
Lecture 18 Confidence Intervals (Code) 00:02:07 Duration
Lecture 19 Statistical Testing 00:14:08 Duration
Lecture 20 Statistical Testing (Code) 00:06:57 Duration
Lecture 21 Covariance and Correlation 00:08:06 Duration
Lecture 22 Covariance and Correlation (Code) 00:05:46 Duration
Lecture 23 Alpha and Beta 00:06:45 Duration
Lecture 24 Alpha and Beta (Code) 00:07:59 Duration
Lecture 25 Mixture of Gaussians 00:06:31 Duration
Lecture 26 Mixture of Gaussians (Code) 00:06:03 Duration
Lecture 27 Volatility Clustering 00:02:54 Duration
Lecture 28 Price Simulation 00:02:54 Duration
Lecture 29 Price Simulation (Code) 00:02:24 Duration
Lecture 30 Financial Basics Section Summary 00:02:11 Duration
Lecture 31 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 3 : Time Series Analysis
Lecture 1 Time Series Analysis Section Introduction 00:06:42 Duration
Lecture 2 Efficient Market Hypothesis 00:11:08 Duration
Lecture 3 Random Walk Hypothesis 00:14:15 Duration
Lecture 4 The Naive Forecast 00:06:35 Duration
Lecture 5 Simple Moving Average (Theory) 00:04:08 Duration
Lecture 6 Simple Moving Average (Code) 00:08:31 Duration
Lecture 7 Exponentially-Weighted Moving Average (Theory) 00:10:58 Duration
Lecture 8 Exponentially-Weighted Moving Average (Code) 00:10:54 Duration
Lecture 9 Simple Exponential Smoothing for Forecasting (Theory) 00:10:03 Duration
Lecture 10 Simple Exponential Smoothing for Forecasting (Code) 00:10:14 Duration
Lecture 11 Holt's Linear Trend Model (Theory) 00:07:46 Duration
Lecture 12 Holt's Linear Trend Model (Code) 00:03:01 Duration
Lecture 13 Holt-Winters (Theory) 00:11:10 Duration
Lecture 14 Holt-Winters (Code) 00:07:50 Duration
Lecture 15 Autoregressive Models - AR(p) 00:12:41 Duration
Lecture 16 Moving Average Models - MA(q) 00:03:22 Duration
Lecture 17 ARIMA 00:10:35 Duration
Lecture 18 ARIMA in Code (pt 1) 00:20:15 Duration
Lecture 19 Stationarity 00:12:11 Duration
Lecture 20 Stationarity Code 00:09:40 Duration
Lecture 21 ACF (Autocorrelation Function) 00:10:01 Duration
Lecture 22 PACF (Partial Autocorrelation Funtion) 00:06:45 Duration
Lecture 23 ACF and PACF in Code (pt 1) 00:08:16 Duration
Lecture 24 ACF and PACF in Code (pt 2) 00:06:53 Duration
Lecture 25 Auto ARIMA and SARIMAX 00:09:32 Duration
Lecture 26 Model Selection, AIC and BIC 00:09:43 Duration
Lecture 27 ARIMA in Code (pt 2) 00:14:29 Duration
Lecture 28 ARIMA in Code (pt 3) 00:16:11 Duration
Lecture 29 ACF and PACF for Stock Returns 00:07:25 Duration
Lecture 30 Forecasting 00:09:04 Duration
Lecture 31 Time Series Analysis Section Conclusion 00:04:03 Duration

Section 4 : Portfolio Optimization and CAPM
Lecture 1 Portfolio Optimization Section Introduction 00:03:25 Duration
Lecture 2 The S&P500 00:02:37 Duration
Lecture 3 What is Risk 00:06:53 Duration
Lecture 4 Why Diversify
Lecture 5 Describing a Portfolio (pt 1) 00:09:42 Duration
Lecture 6 Describing a Portfolio (pt 2) 00:06:21 Duration
Lecture 7 Visualizing Random Portfolios and Monte Carlo Simulation (pt 1) 00:12:58 Duration
Lecture 8 Visualizing Random Portfolios and Monte Carlo Simulation (pt 2)
Lecture 9 Maximum and Minimum Portfolio Return 00:09:25 Duration
Lecture 10 Maximum and Minimum Portfolio Return in Code 00:04:49 Duration
Lecture 11 Mean-Variance Optimization 00:07:17 Duration
Lecture 12 The Efficient Frontier 00:07:14 Duration
Lecture 13 Mean-Variance Optimization And The Efficient Frontier in Code 00:09:04 Duration
Lecture 14 Global Minimum Variance (GMV) Portfolio 00:01:47 Duration
Lecture 15 Global Minimum Variance (GMV) Portfolio in Code 00:02:05 Duration
Lecture 16 Sharpe Ratio 00:07:52 Duration
Lecture 17 Maximum Sharpe Ratio in Code 00:06:25 Duration
Lecture 18 Portfolio with a Risk-Free Asset and Tangency Portfolio 00:09:42 Duration
Lecture 19 Risk-Free Asset and Tangency Portfolio in Code 00:02:06 Duration
Lecture 20 Capital Asset Pricing Model (CAPM) 00:12:17 Duration
Lecture 21 Problems with Markowitz Portfolio Theory and Robust Estimation 00:09:03 Duration
Lecture 22 Portfolio Optimization Section Conclusion 00:02:15 Duration

Section 5 : VIP Algorithmic Trading
Lecture 1 Algorithmic Trading Section Introduction 00:02:45 Duration
Lecture 2 Trend-Following Strategy 00:13:05 Duration
Lecture 3 Trend-Following Strategy in Code (pt 1) 00:08:17 Duration
Lecture 4 Trend-Following Strategy in Code (pt 2) 00:09:28 Duration
Lecture 5 Machine Learning-Based Trading Strategy 00:07:44 Duration
Lecture 6 Machine Learning-Based Trading Strategy in Code 00:09:15 Duration
Lecture 7 Classification-Based Trading Strategy in Code 00:03:30 Duration
Lecture 8 Using a Random Forest Classifier for Machine Learning-Based Trading 00:04:51 Duration
Lecture 9 Algorithmic Trading Section Summary 00:05:46 Duration

Section 6 : VIP The Basics of Reinforcement Learning
Lecture 1 Reinforcement Learning Section Introduction 00:06:24 Duration
Lecture 2 Elements of a Reinforcement Learning Problem 00:20:08 Duration
Lecture 3 States, Actions, Rewards, Policies 00:09:14 Duration
Lecture 4 Markov Decision Processes (MDPs) 00:09:57 Duration
Lecture 5 The Return 00:04:46 Duration
Lecture 6 Value Functions and the Bellman Equation 00:09:43 Duration
Lecture 7 What does it mean to “learn” 00:07:08 Duration
Lecture 8 Solving the Bellman Equation with Reinforcement Learning (pt 1) 00:09:38 Duration
Lecture 9 Solving the Bellman Equation with Reinforcement Learning (pt 2) 00:11:51 Duration
Lecture 10 Epsilon-Greedy 00:05:59 Duration
Lecture 11 Q-Learning 00:14:06 Duration
Lecture 12 How to Learn Reinforcement Learning 00:05:46 Duration

Section 7 : VIP Reinforcement Learning for Algorithmic Trading
Lecture 1 Trend-Following Strategy with Reinforcement Learning API 00:12:23 Duration
Lecture 2 Trend-Following Strategy Revisited (Code) 00:09:04 Duration
Lecture 3 Q-Learning in an Algorithmic Trading Context
Lecture 4 Representing States 00:07:17 Duration
Lecture 5 Q-Learning for Algorithmic Trading in Code 00:15:23 Duration

Section 8 : VIP Statistical Factor Models and Unsupervised Machine Learning
Lecture 1 Statistical Factor Models (Beginner) 00:15:31 Duration
Lecture 2 Statistical Factor Models (Intermediate) 00:10:00 Duration
Lecture 3 Statistical Factor Models (Advanced) 00:19:40 Duration
Lecture 4 Statistical Factor Models (Code) 00:16:04 Duration

Section 9 : VIP Regime Detection and Sequence Modeling with Hidden Markov Models
Lecture 1 Why Sequence Models (pt 1) 00:13:57 Duration
Lecture 2 Why Sequence Models (pt 2) 00:12:04 Duration
Lecture 3 HMM Parameters 00:09:16 Duration
Lecture 4 HMM Tasks and the Viterbi Algorithm 00:15:06 Duration
Lecture 5 HMM for Modeling Volatility Clustering in Code 00:18:29 Duration

Section 10 : Extras
Lecture 1 About Proctor Testing
Lecture 2 About Certification

Section 11 : Setting Up Your Environment FAQ
Lecture 1 Windows-Focused Environment Setup 2018 00:20:13 Duration
Lecture 2 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 00:17:30 Duration

Section 12 : Extra Help With Python Coding for Beginners FAQ
Lecture 1 How to Code by Yourself (part 1) 00:15:49 Duration
Lecture 2 How to Code by Yourself (part 2) 00:09:23 Duration
Lecture 3 Proof that using Jupyter Notebook is the same as not using it 00:12:24 Duration

Section 13 : Effective Learning Strategies for Machine Learning FAQ
Lecture 1 How to Succeed in this Course (Long Version) 00:10:17 Duration
Lecture 2 Is this for Beginners or Experts Academic or Practical Fast or slow-paced 00:21:57 Duration
Lecture 3 Machine Learning and AI Prerequisite Roadmap (pt 1) 00:11:14 Duration
Lecture 4 Machine Learning and AI Prerequisite Roadmap (pt 2) 00:16:07 Duration

Section 14 : Appendix FAQ Finale
Lecture 1 What is the Appendix 00:02:41 Duration