2022 Python for Machine Learning & Data Science Masterclass

44h 5m 31s
English
Paid
August 27, 2024

Welcome to the most complete course on learning Data Science and Machine Learning on the internet! After teaching over 2 million students I've worked for over a year to put together what I believe to be the best way to go from zero to hero for data science and machine learning in Python! This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Data Science and Machine Learning.

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The typical starting salary for a data scientists can be over $150,000 dollars, and we've created this course to help guide students to learning a set of skills to make them extremely hirable in today's workplace environment.

We'll cover everything you need to know for the full data science and machine learning tech stack required at the world's top companies. Our students have gotten jobs at McKinsey, Facebook, Amazon, Google, Apple, Asana, and other top tech companies! We've structured the course using our experience teaching both online and in-person to deliver a clear and structured approach that will guide you through understanding not just how to use data science and machine learning libraries, but why we use them. This course is balanced between practical real world case studies and mathematical theory behind the machine learning algorithms.

We cover advanced machine learning algorithms that most other courses don't! Including advanced regularization methods and state of the art unsupervised learning methods, such as DBSCAN.

This comprehensive course is designed to be on par with Bootcamps that usually cost thousands of dollars and includes the following topics:

  • Programming with Python

  • NumPy with Python

  • Deep dive into Pandas for Data Analysis

  • Full understanding of Matplotlib Programming Library

  • Deep dive into seaborn for data visualizations

  • Machine Learning with SciKit Learn, including:

    • Linear Regression

    • Regularization

    • Lasso Regression

    • Ridge Regression

    • Elastic Net

    • K Nearest Neighbors

    • K Means Clustering

    • Decision Trees

    • Random Forests

    • Natural Language Processing

    • Support Vector Machines

    • Hierarchal Clustering

    • DBSCAN

    • PCA

    • Model Deployment

    • and much, much more!

As always, we're grateful for the chance to teach you data science, machine learning, and python and hope you will join us inside the course to boost your skillset!

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# Title Duration
1 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP! 04:18
2 Anaconda Python and Jupyter Install and Setup 13:50
3 Environment Setup 09:09
4 Python Crash Course - Part One 16:08
5 Python Crash Course - Part Two 12:08
6 Python Crash Course - Part Three 11:20
7 Python Crash Course - Exercise Questions 01:30
8 Python Crash Course - Exercise Solutions 09:27
9 Machine Learning Pathway 10:17
10 Introduction to NumPy 02:15
11 NumPy Arrays 22:42
12 NumPy Indexing and Selection 11:07
13 NumPy Operations 08:15
14 NumPy Exercises 01:19
15 Numpy Exercises - Solutions 07:06
16 Introduction to Pandas 04:41
17 Series - Part One 09:29
18 Series - Part Two 10:42
19 DataFrames - Part One - Creating a DataFrame 19:28
20 DataFrames - Part Two - Basic Properties 08:19
21 DataFrames - Part Three - Working with Columns 13:58
22 DataFrames - Part Four - Working with Rows 14:31
23 Pandas - Conditional Filtering 17:42
24 Pandas - Useful Methods - Apply on Single Column 13:48
25 Pandas - Useful Methods - Apply on Multiple Columns 17:24
26 Pandas - Useful Methods - Statistical Information and Sorting 15:49
27 Missing Data - Overview 12:00
28 Missing Data - Pandas Operations 18:33
29 GroupBy Operations - Part One 15:50
30 GroupBy Operations - Part Two - MultiIndex 14:19
31 Combining DataFrames - Concatenation 10:25
32 Combining DataFrames - Inner Merge 12:05
33 Combining DataFrames - Left and Right Merge 06:08
34 Combining DataFrames - Outer Merge 10:39
35 Pandas - Text Methods for String Data 16:06
36 Pandas - Time Methods for Date and Time Data 21:01
37 Pandas Input and Output - CSV Files 10:21
38 Pandas Input and Output - HTML Tables 14:42
39 Pandas Input and Output - Excel Files 07:21
40 Pandas Input and Output - SQL Databases 18:20
41 Pandas Pivot Tables 21:16
42 Pandas Project Exercise Overview 05:27
43 Pandas Project Exercise Solutions 26:32
44 Introduction to Matplotlib 04:07
45 Matplotlib Basics 12:36
46 Matplotlib - Understanding the Figure Object 07:33
47 Matplotlib - Implementing Figures and Axes 14:32
48 Matplotlib - Figure Parameters 04:57
49 Matplotlib - Subplots Functionality 19:18
50 Matplotlib Styling - Legends 07:03
51 Matplotlib Styling - Colors and Styles 14:30
52 Advanced Matplotlib Commands (Optional) 03:53
53 Matplotlib Exercise Questions Overview 06:11
54 Matplotlib Exercise Questions - Solutions 16:40
55 Introduction to Seaborn 03:55
56 Scatterplots with Seaborn 18:20
57 Distribution Plots - Part One - Understanding Plot Types 09:36
58 Distribution Plots - Part Two - Coding with Seaborn 16:15
59 Categorical Plots - Statistics within Categories - Understanding Plot Types 05:41
60 Categorical Plots - Statistics within Categories - Coding with Seaborn 09:16
61 Categorical Plots - Distributions within Categories - Understanding Plot Types 13:21
62 Categorical Plots - Distributions within Categories - Coding with Seaborn 17:58
63 Seaborn - Comparison Plots - Understanding the Plot Types 05:33
64 Seaborn - Comparison Plots - Coding with Seaborn 09:48
65 Seaborn Grid Plots 13:40
66 Seaborn - Matrix Plots 13:19
67 Seaborn Plot Exercises Overview 06:45
68 Seaborn Plot Exercises Solutions 14:34
69 Capstone Project Overview 12:49
70 Capstone Project Solutions - Part One 17:16
71 Capstone Project Solutions - Part Two 14:51
72 Capstone Project Solutions - Part Three 19:50
73 Introduction to Machine Learning Overview Section 05:14
74 Why Machine Learning? 09:16
75 Types of Machine Learning Algorithms 07:48
76 Supervised Machine Learning Process 13:42
77 Companion Book - Introduction to Statistical Learning 02:53
78 Introduction to Linear Regression Section 01:40
79 Linear Regression - Algorithm History 09:23
80 Linear Regression - Understanding Ordinary Least Squares 15:44
81 Linear Regression - Cost Functions 08:13
82 Linear Regression - Gradient Descent 12:00
83 Python coding Simple Linear Regression 19:38
84 Overview of Scikit-Learn and Python 08:27
85 Linear Regression - Scikit-Learn Train Test Split 15:49
86 Linear Regression - Scikit-Learn Performance Evaluation - Regression 15:45
87 Linear Regression - Residual Plots 13:58
88 Linear Regression - Model Deployment and Coefficient Interpretation 17:47
89 Polynomial Regression - Theory and Motivation 08:00
90 Polynomial Regression - Creating Polynomial Features 10:55
91 Polynomial Regression - Training and Evaluation 09:45
92 Bias Variance Trade-Off 10:35
93 Polynomial Regression - Choosing Degree of Polynomial 13:38
94 Polynomial Regression - Model Deployment 06:08
95 Regularization Overview 06:40
96 Feature Scaling 10:00
97 Introduction to Cross Validation 12:54
98 Regularization Data Setup 08:38
99 L2 Regularization - Ridge Regression Theory 14:30
100 L2 Regularization - Ridge Regression - Python Implementation 17:43
101 L1 Regularization - Lasso Regression - Background and Implementation 15:03
102 L1 and L2 Regularization - Elastic Net 18:08
103 Linear Regression Project - Data Overview 04:31
104 Introduction to Feature Engineering and Data Preparation 15:29
105 Dealing with Outliers 26:34
106 Dealing with Missing Data : Part One - Evaluation of Missing Data 10:43
107 Dealing with Missing Data : Part Two - Filling or Dropping data based on Rows 20:41
108 Dealing with Missing Data : Part 3 - Fixing data based on Columns 23:17
109 Dealing with Categorical Data - Encoding Options 12:48
110 Section Overview and Introduction 03:15
111 Cross Validation - Test | Train Split 11:21
112 Cross Validation - Test | Validation | Train Split 14:49
113 Cross Validation - cross_val_score 11:38
114 Cross Validation - cross_validate 06:57
115 Grid Search 12:15
116 Linear Regression Project Overview 03:27
117 Linear Regression Project - Solutions 12:11
118 Introduction to Logistic Regression Section 05:28
119 Logistic Regression - Theory and Intuition - Part One: The Logistic Function 05:37
120 Logistic Regression - Theory and Intuition - Part Two: Linear to Logistic 04:55
121 Logistic Regression - Theory and Intuition - Linear to Logistic Math 17:01
122 Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood 15:43
123 Logistic Regression with Scikit-Learn - Part One - EDA 13:58
124 Logistic Regression with Scikit-Learn - Part Two - Model Training 06:39
125 Classification Metrics - Confusion Matrix and Accuracy 09:46
126 Classification Metrics - Precison, Recall, F1-Score 06:01
127 Classification Metrics - ROC Curves 07:14
128 Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation 15:57
129 Multi-Class Classification with Logistic Regression - Part One - Data and EDA 08:08
130 Multi-Class Classification with Logistic Regression - Part Two - Model 15:48
131 Logistic Regression Exercise Project Overview 04:00
132 Logistic Regression Project Exercise - Solutions 21:37
133 Introduction to KNN Section 02:12
134 KNN Classification - Theory and Intuition 11:19
135 KNN Coding with Python - Part One 13:41
136 KNN Coding with Python - Part Two - Choosing K 23:26
137 KNN Classification Project Exercise Overview 03:19
138 KNN Classification Project Exercise Solutions 14:13
139 Introduction to Support Vector Machines 01:30
140 History of Support Vector Machines 04:42
141 SVM - Theory and Intuition - Hyperplanes and Margins 13:26
142 SVM - Theory and Intuition - Kernel Intuition 04:58
143 SVM - Theory and Intuition - Kernel Trick and Mathematics 20:51
144 SVM with Scikit-Learn and Python - Classification Part One 11:00
145 SVM with Scikit-Learn and Python - Classification Part Two 16:03
146 SVM with Scikit-Learn and Python - Regression Tasks 21:00
147 Support Vector Machine Project Overview 04:28
148 Support Vector Machine Project Solutions 18:32
149 Introduction to Tree Based Methods 01:23
150 Decision Tree - History 09:05
151 Decision Tree - Terminology 04:13
152 Decision Tree - Understanding Gini Impurity 07:53
153 Constructing Decision Trees with Gini Impurity - Part One 07:33
154 Constructing Decision Trees with Gini Impurity - Part Two 11:25
155 Coding Decision Trees - Part One - The Data 19:19
156 Coding Decision Trees - Part Two -Creating the Model 20:57
157 Introduction to Random Forests Section 01:47
158 Random Forests - History and Motivation 11:39
159 Random Forests - Key Hyperparameters 03:00
160 Random Forests - Number of Estimators and Features in Subsets 10:57
161 Random Forests - Bootstrapping and Out-of-Bag Error 12:47
162 Coding Classification with Random Forest Classifier - Part One 11:37
163 Coding Classification with Random Forest Classifier - Part Two 22:23
164 Coding Regression with Random Forest Regressor - Part One - Data 04:29
165 Coding Regression with Random Forest Regressor - Part Two - Basic Models 13:34
166 Coding Regression with Random Forest Regressor - Part Three - Polynomials 10:31
167 Coding Regression with Random Forest Regressor - Part Four - Advanced Models 10:37
168 Introduction to Boosting Section 01:48
169 Boosting Methods - Motivation and History 06:12
170 AdaBoost Theory and Intuition 19:52
171 AdaBoost Coding Part One - The Data 11:14
172 AdaBoost Coding Part Two - The Model 18:10
173 Gradient Boosting Theory 10:23
174 Gradient Boosting Coding Walkthrough 12:49
175 Introduction to Supervised Learning Capstone Project 14:24
176 Solution Walkthrough - Supervised Learning Project - Data and EDA 18:19
177 Solution Walkthrough - Supervised Learning Project - Cohort Analysis 23:10
178 Solution Walkthrough - Supervised Learning Project - Tree Models 21:24
179 Introduction to NLP and Naive Bayes Section 02:37
180 Naive Bayes Algorithm - Part One - Bayes Theorem 08:05
181 Naive Bayes Algorithm - Part Two - Model Algorithm 17:56
182 Feature Extraction from Text - Part One - Theory and Intuition 10:34
183 Feature Extraction from Text - Coding Count Vectorization Manually 18:54
184 Feature Extraction from Text - Coding with Scikit-Learn 11:25
185 Natural Language Processing - Classification of Text - Part One 11:24
186 Natural Language Processing - Classification of Text - Part Two 10:19
187 Text Classification Project Exercise Overview 04:38
188 Text Classification Project Exercise Solutions 15:38
189 Unsupervised Learning Overview 08:18
190 Introduction to K-Means Clustering Section 02:15
191 Clustering General Overview 10:37
192 K-Means Clustering Theory 11:31
193 K-Means Clustering - Coding Part One 19:49
194 K-Means Clustering Coding Part Two 17:19
195 K-Means Clustering Coding Part Three 14:33
196 K-Means Color Quantization - Part One 13:54
197 K-Means Color Quantization - Part Two 14:34
198 K-Means Clustering Exercise Overview 07:48
199 K-Means Clustering Exercise Solution - Part One 13:11
200 K-Means Clustering Exercise Solution - Part Two 15:52
201 K-Means Clustering Exercise Solution - Part Three 08:21
202 Introduction to Hierarchical Clustering 00:51
203 Hierarchical Clustering - Theory and Intuition 11:49
204 Hierarchical Clustering - Coding Part One - Data and Visualization 16:13
205 Hierarchical Clustering - Coding Part Two - Scikit-Learn 28:23
206 Introduction to DBSCAN Section 01:01
207 DBSCAN - Theory and Intuition 17:27
208 DBSCAN versus K-Means Clustering 12:24
209 DBSCAN - Hyperparameter Theory 07:16
210 DBSCAN - Hyperparameter Tuning Methods 21:56
211 DBSCAN - Outlier Project Exercise Overview 05:56
212 DBSCAN - Outlier Project Exercise Solutions 23:21
213 Introduction to Principal Component Analysis 02:48
214 PCA Theory and Intuition - Part One 10:25
215 PCA Theory and Intuition - Part Two 11:13
216 PCA - Manual Implementation in Python 18:17
217 PCA - SciKit-Learn 12:10
218 PCA - Project Exercise Overview 07:22
219 PCA - Project Exercise Solution 17:04
220 Model Deployment Section Overview 02:20
221 Model Deployment Considerations 06:52
222 Model Persistence 21:08
223 Model Deployment as an API - General Overview 07:42
224 Model API - Creating the Script 17:01
225 Testing the API 07:50

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