Section 1 : Introduction and Outline

 		Lecture 1 Introduction and Outline Why would you want to use an HMM copy 4:4
Lecture 2 Unsupervised or Supervised 2:59
Lecture 3 Where to get the Code and Data
Lecture 4 Anyone Can Succeed in this Course 11:55

Section 2 : Markov Models

 		Lecture 5 The Markov Property 4:40
Lecture 6 Markov Models 7:2
Lecture 7 The Math of Markov Chains 5:16

Section 3 : Markov Models Example Problems and Applications

 		Lecture 8 Example Problem Sick or Healthy 3:26
Lecture 9 Example Problem Expected number of continuously sick days 2:53
Lecture 10 Example application SEO and Bounce Rate Optimization 8:54
Lecture 11 Example Application Build a 2nd-order language model and generate phrases 13:7
Lecture 12 Example Application Google’s PageRank algorithm 5:4
Lecture 13 Suggestion Box 2:26

Section 4 : Hidden Markov Models for Discrete Observations

 		Lecture 14 From Markov Models to Hidden Markov Models 6:2
Lecture 15 HMM - Basic Examples 8:4
Lecture 16 Parameters of an HMM 7:1
Lecture 17 The 3 Problems of an HMM 5:43
Lecture 18 The Forward-Backward Algorithm (part 1) 16:59
Lecture 19 The Forward-Backward Algorithm (part 2) 7:9
Lecture 20 The Forward-Backward Algorithm (part 3) 7:18
Lecture 21 The Viterbi Algorithm (part 1) 6:14
Lecture 22 The Viterbi Algorithm (part 2) 15:5
Lecture 23 HMM Training (part 1) 4:41
Lecture 24 HMM Training (part 2) 10:22
Lecture 25 HMM Training (part 3) 13:34
Lecture 26 HMM Training (part 4) 13:17
Lecture 27 How to Choose the Number of Hidden States 7:2
Lecture 28 Baum-Welch Updates for Multiple Observations 4:54
Lecture 29 Discrete HMM in Code 20:33
Lecture 30 The underflow problem and how to solve it 5:5
Lecture 31 Discrete HMM Updates in Code with Scaling 11:53
Lecture 32 Scaled Viterbi Algorithm in Log Space 3:39

Section 5 : Discrete HMMs Using Deep Learning Libraries

 		Lecture 33 Gradient Descent Tutorial 4:30
Lecture 34 Theano Scan Tutorial 12:40
Lecture 35 Discrete HMM in Theano
Lecture 36 Improving our Gradient Descent-Based HMM 5:10
Lecture 37 Tensorflow Scan Tutorial 12:43
Lecture 38 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 6 : HMMs for Continuous Observations

 		Lecture 39 Gaussian Mixture Models with Hidden Markov Models 4:12
Lecture 40 Generating Data from a Real-Valued HMM 6:35
Lecture 41 Continuous-Observation HMM in Code (part 1) 18:38
Lecture 42 Continuous-Observation HMM in Code (part 2) 5:13
Lecture 43 Continuous HMM in Theano 16:33
Lecture 44 Continuous HMM in Tensorflow 9:26

Section 7 : HMMs for Classification

 		Lecture 45 Generative vs 2:30
Lecture 46 HMM Classification on Poetry Data (Robert Frost vs

Section 8 : Bonus Example Parts-of-Speech Tagging

 		Lecture 47 Parts-of-Speech Tagging Concepts 5:0
Lecture 48 POS Tagging with an HMM 5:58

Section 9 : Theano, Tensorflow, and Machine Learning Basics Review

 		Lecture 49 (Review) Gaussian Mixture Models 3:5
Lecture 50 (Review) Theano Tutorial 7:47
Lecture 51 (Review) Tensorflow Tutorial 7:27

Section 10 : Setting Up Your Environment (FAQ by Student Request)

 		Lecture 52 Windows-Focused Environment Setup 2018 20:20
Lecture 53 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:33

Section 11 : Extra Help With Python Coding for Beginners (FAQ by Student Request)

 		Lecture 54 How to Code by Yourself (part 1)
Lecture 55 How to Code by Yourself (part 2) 9:23
Lecture 56 Proof that using Jupyter Notebook is the same as not using it 12:29
Lecture 57 Python 2 vs Python 3 4:29

Section 12 : Effective Learning Strategies for Machine Learning (FAQ by Student Request)

 		Lecture 58 How to Succeed in this Course (Long Version) 10:24
Lecture 59 Is this for Beginners or Experts Academic or Practical Fast or slow-paced
Lecture 60 Machine Learning and AI Prerequisite Roadmap (pt 1) 11:15
Lecture 61 Machine Learning and AI Prerequisite Roadmap (pt 2) 16:7

Section 13 : Appendix FAQ Finale

 		Lecture 62 What is the Appendix 2:39