Section 1 : Course Introduction

lecture 1 Introduction to the Course 3:3
lecture 2 Course Help and Welcome 0:36
lecture 3 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 2 : Environment Set-Up

lecture 4 Python Environment Setup 9:52

Section 3 : Jupyter Overview

lecture 5 Updates to Notebook Text
lecture 6 Jupyter Notebooks 13:13
lecture 7 Optional Virtual Environments 9:51

Section 4 : Python Crash Course

lecture 8 Welcome to the Python Crash Course Section! 0:17
lecture 9 Introduction to Python Crash Course 1:18
lecture 10 Python Crash Course - Part 1 19:19
lecture 11 Python Crash Course - Part 2 15:8
lecture 12 Python Crash Course - Part 3 15:43
lecture 13 Python Crash Course - Part 4 15:24
lecture 14 Python Crash Course Exercises - Overview
lecture 15 Python Crash Course Exercises - Solutions 11:51

Section 5 : Python for Data Analysis - NumPy

lecture 16 Welcome to the NumPy Section!. 0:11
lecture 17 Introduction to Numpy
lecture 18 Numpy Arrays 16:44
lecture 19 Quick Note on Array Indexing Text
lecture 20 Numpy Array Indexing 18:17
lecture 21 Numpy Operations 6:58
lecture 22 Numpy Exercises Overview 2:38
lecture 23 Numpy Exercises Solutions 15:26

Section 6 : Python for Data Analysis - Pandas

lecture 24 Welcome to the Pandas Section! 0:14
lecture 25 Introduction to Pandas 1:37
lecture 26 Series 10:34
lecture 27 DataFrames - Part 1 15:25
lecture 28 DataFrames - Part 2 17:4
lecture 29 DataFrames - Part 3 9:6
lecture 30 Missing Data 6:14
lecture 31 Groupby 6:44
lecture 32 Merging Joining and Concatenating 8:50
lecture 33 Operations 11:59
lecture 34 Data Input and Output 13:54

Section 7 : Python for Data Analysis - Pandas Exercises

lecture 35 Note on SF Salary Exercise Text
lecture 36 SF Salaries Exercise Overview 1:44
lecture 37 SF Salaries Solutions 15:20
lecture 38 Ecommerce Purchases Exercise Overview 2:5
lecture 39 Ecommerce Purchases Exercise Solutions 15:7

Section 8 : Python for Data Visualization - Matplotlib

lecture 40 Welcome to the Data Visualization Section! 0:22
lecture 41 Introduction to Matplotlib 2:55
lecture 42 Matplotlib Part 1 16:33
lecture 43 Matplotlib Part 2 15:45
lecture 44 Matplotlib Part 3 11:46
lecture 45 Matplotlib Exercises Overview 1:40
lecture 46 Matplotlib Exercises - Solutions 10:14

Section 9 : Python for Data Visualization - Seaborn

lecture 47 Introduction to Seaborn 2:53
lecture 48 Distribution Plots 18:14
lecture 49 Categorical Plots 17:12
lecture 50 Matrix Plots 10:9
lecture 51 Grids 8:25
lecture 52 Regression Plots 7:8
lecture 53 Style and Color 8:16
lecture 54 Seaborn Exercise Overview 1:47
lecture 55 Seaborn Exercise Solutions 7:3

Section 10 : Python for Data Visualization - Pandas Built-in Data Visuali

lecture 56 Pandas Built-in Data Visualization 13:22
lecture 57 Pandas Data Visualization Exercise 1:15
lecture 58 Pandas Data Visualization Exercise- Solutions 8:52

Section 11 : Python for Data Visualization - Plotly and Cufflinks

lecture 59 Introduction to Plotly and Cufflinks 3:17
lecture 60 READ ME FIRST BEFORE PLOTLY PLEASE! Text
lecture 61 Plotly and Cufflinks 18:33

Section 12 : Python for Data Visualization - Geographical Plotting

lecture 62 Introduction to Geographical Plotting 0:49
lecture 63 Choropleth Maps - Part 1 - USA 19:21
lecture 64 Choropleth Maps - Part 2 - World 6:48
lecture 65 Choropleth Exercises 3:6
lecture 66 Choropleth Exercises - Solutions 9:56

Section 13 : Data Capstone Project

lecture 67 Welcome to the Data Capstone Projects! 0:17
lecture 68 911 Calls Project Overview 2:1
lecture 69 911 Calls Solutions - Part 1 14:23
lecture 70 911 Calls Solutions - Part 2 17:32
lecture 71 Bank Data Text
lecture 72 Finance Data Project Overview 2:41
lecture 73 Finance Project - Solutions Part 1 16:7
lecture 74 Finance Project - Solutions Part 2 18:4
lecture 75 Finance Project - Solutions Part 3 6:18

Section 14 : Introduction to Machine Learning

lecture 76 About Certification Pdf
lecture 77 Welcome to the Machine Learning Section! 0:31
lecture 78 Supervised Learning Overview 8:13
lecture 79 Evaluating Performance - Classification Error 16:30
lecture 80 Evaluating Performance - Regression Error Met 5:29
lecture 81 Machine Learning with Python 9:21

Section 15 : Linear Regression

lecture 82 Linear Regression Theory 4:28
lecture 83 model_selection Updates for SciKit Learn 0.18 Text
lecture 84 Linear Regression with Python - Part 1 18:11
lecture 85 Linear Regression with Python - Part 2 6:58
lecture 86 Linear Regression Project Overview 2:26
lecture 87 Linear Regression Project Solution 18:31

Section 16 : Cross Validation and Bias-Variance Trade-Off

lecture 88 Bias Variance Trade-Off 6:20

Section 17 : Logistic Regression

lecture 89 Logistic Regression Theory 11:47
lecture 90 Logistic Regression with Python - Part 1 17:37
lecture 91 Logistic Regression with Python - Part 2 16:50
lecture 92 Logistic Regression with Python - Part 3 8:9
lecture 93 Logistic Regression Project Overview 1:30
lecture 94 Logistic Regression Project Solutions 11:0

Section 18 : K Nearest Neighbors

lecture 95 KNN Theory 5:33
lecture 96 KNN with Python 19:34
lecture 97 KNN Project Overview 1:7
lecture 98 KNN Project Solutions 14:9

Section 19 : Decision Trees and Random Forests

lecture 99 Introduction to Tree Methods 6:48
lecture 100 Decision Trees and Random Forest with Python 13:52
lecture 101 Decision Trees and Random Forest Project Over 3:4
lecture 102 Decision Trees and Random Forest Solutions 11:53
lecture 103 Decision Trees and Random Forest Solutions 8:39

Section 20 : Support Vector Machines

lecture 104 SVM Theory 4:31
lecture 105 Support Vector Machines with Python 17:47
lecture 106 SVM Project Overview 2:14
lecture 107 SVM Project Solutions 10:4

Section 21 : K Means Clustering

lecture 108 K Means Algorithm Theory 5:9
lecture 109 K Means with Python 12:30
lecture 110 K Means Project Overview 2:46
lecture 111 K Means Project Solutions 16:32

Section 22 : Principal Component Analysis

lecture 112 Principal Component Analysis 3:20
lecture 113 PCA with Python 16:19

Section 23 : Recommender Systems

lecture 114 Recommender Systems 4:7
lecture 115 Recommender Systems with Python - Part 1 13:31
lecture 116 Recommender Systems with Python - Part 2 13:16

Section 24 : Natural Language Processing

lecture 117 Natural Language Processing Theory 5:0
lecture 118 NLP with Python - Part 1 15:57
lecture 119 NLP with Python - Part 2
lecture 120 NLP with Python - Part 3 17:24
lecture 121 NLP Project Overview 1:59
lecture 122 NLP Project Solutions 19:20

Section 25 : Neural Nets and Deep Learning

lecture 123 About Proctor Testing Pdf
lecture 124 Welcome to the Deep Learning Section!
lecture 125 Introduction to Artificial Neural Networks 2:2
lecture 126 Installing Tensorflow Text
lecture 127 Perceptron Model
lecture 128 Neural Networks 7:13
lecture 129 Activation Functions 10:33
lecture 130 Multi-Class Classification Considerations 10:27
lecture 131 Cost Functions and Gradient Descent 18:6
lecture 132 Backpropagation 14:42
lecture 133 TensorFlow vs Keras 2:7
lecture 134 TF Syntax Basics - Part One - Preparing the 10:43
lecture 135 TF Syntax Basics - Part Two - Creating and 13:54
lecture 136 TF Syntax Basics - Part Three - Model Evaluat 12:51
lecture 137 TF Regression Code Along - Exploratory Data 18:43
lecture 138 TF Regression Code Along - Exploratory Data 13:11
lecture 139 TF Regression Code Along - Data Preprocessing 8:23
lecture 140 TF Regression Code Along - Model Evaluation 11:18
lecture 141 TF Classification Code Along - EDA and Prepro 7:59
lecture 142 TF Classification - Dealing with Overfitting 16:43
lecture 143 TensorFlow 2.0 Project Options Overview 1:33
lecture 144 TensorFlow 2.0 Project Notebook Overview 7:36
lecture 145 Keras Project Solutions - Dealing with Missin 20:29
lecture 146 Keras Project Solutions - Dealing with Missin 14:40
lecture 147 Keras Project Solutions - Categorical Data 11:56
lecture 148 Keras Project Solutions - Data PreProcessing 17:16
lecture 149 Keras Project Solutions - Data PreProcessing 3:40
lecture 150 Keras Project Solutions - Creating and Training 3:51
lecture 151 Keras Project Solutions - Model Evaluation 9:36
lecture 152 Tensorboard 18:17

Section 26 : Big Data and Spark with Python

lecture 153 Welcome to the Big Data Section! 0:23
lecture 154 Big Data Overview 5:26
lecture 155 Spark Overview 8:54
lecture 156 Local Spark Set-Up Text
lecture 157 AWS Account Set-Up 4:6
lecture 158 Quick Note on AWS Security Text
lecture 159 EC2 Instance Set-Up 16:3
lecture 160 SSH with Mac or Linux 4:48
lecture 161 PySpark Setup 23:43
lecture 162 Lambda Expressions Review 5:21
lecture 163 Introduction to Spark and Python 8:16
lecture 164 RDD Transformations and Actions 23:7