2022 Python for Machine Learning & Data Science Masterclass

44h 5m 31s
English
Paid

Course description

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.

Read more about the course

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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#1: COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!

All Course Lessons (225)

#Lesson TitleDurationAccess
1
COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP! Demo
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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