CF

2022 Python for Machine Learning & Data Science Masterclass

44h 5m 31s
English
Paid

Welcome to the ultimate course on Data Science and Machine Learning using Python! After educating over 2 million students, I've crafted this comprehensive masterclass to transform your Python skills from basics to mastery in data science and machine learning. This course is tailored for learners who already have a foundation in Python and aspire to deepen their knowledge in utilizing these skills for advanced data science applications.

Why Choose This Course?

The demand for skilled data scientists is incredibly high, with starting salaries surpassing $150,000 in many cases. This course is specifically designed to equip you with an essential skill set to make you highly attractive to today's top employers.

Our students have secured positions at leading companies like McKinsey, Facebook, Amazon, Google, Apple, and Asana. We've meticulously structured this course to offer a coherent pathway that not only demonstrates how to leverage data science and machine learning libraries but also elucidates the underlying principles behind their usage. The course strikes a balance between practical, real-world case studies and the theoretical foundations of machine learning algorithms.

What You'll Learn

This masterclass covers advanced machine learning algorithms often overlooked by other courses, including sophisticated regularization techniques and cutting-edge unsupervised learning methods like DBSCAN. Designed to rival expensive Bootcamps costing thousands of dollars, this course encompasses the following topics:

Core Topics Covered

  • Python Programming Fundamentals
  • Advanced NumPy Techniques
  • Data Analysis with Pandas
  • Mastering Matplotlib for Visualization
  • Seaborn for Detailed Data Visualizations

Machine Learning with SciKit Learn

  • Linear Regression
  • Regularization Techniques
  • Lasso Regression
  • Ridge Regression
  • Elastic Net
  • K Nearest Neighbors
  • K Means Clustering
  • Decision Trees
  • Random Forests
  • Natural Language Processing
  • Support Vector Machines
  • Hierarchical Clustering
  • DBSCAN
  • Principal Component Analysis (PCA)
  • Model Deployment
  • And much, much more!

We sincerely appreciate the opportunity to guide you through this journey of mastering data science, machine learning, and Python. Join us in the course to elevate your skills and achieve your career goals!

About the Author: Udemy

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Udemy is the largest open marketplace for online courses on the internet. Founded in 2010 by Eren Bali, Oktay Caglar, and Gagan Biyani and headquartered in San Francisco, the company went public on the Nasdaq in 2021 under the ticker UDMY. The platform hosts well over two hundred thousand courses across software development, IT and cloud, data science, design, business, marketing, and creative skills, taught by tens of thousands of independent instructors. Roughly seventy million learners use it worldwide, and the corporate arm — Udemy Business — supplies a curated subset of that catalog to enterprise customers.

Because Udemy is a marketplace rather than a single editorial publisher, the catalog is uneven by design. The strongest material lives in the long-form, project-based courses authored by working engineers — full-stack JavaScript, React, Node.js, Python data science, AWS, Docker and Kubernetes, mobile development with Flutter and React Native, and cloud certification preparation. The CourseFlix listing under this source is the slice of that catalog that has been mirrored here for offline-friendly viewing, organized by topic and updated as new releases land. Pricing on Udemy itself swings dramatically with the site's near-permanent sales, which is why the platform is best treated as a deep reference catalog: pick instructors with strong reviews and a track record of updating their material rather than buying on the headline price alone.

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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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Frequently asked questions

What prior knowledge is required to enroll in this course?
Prospective students should have a foundational understanding of Python before enrolling in this course. The course is structured to build on existing Python skills to advance into data science and machine learning applications. While the course includes a Python Crash Course to refresh your skills, it assumes familiarity with basic programming concepts.
What types of projects or exercises will I work on during the course?
Throughout the course, students will engage in practical exercises and projects using data science libraries such as NumPy and Pandas. Students will also participate in a Pandas Project Exercise that consolidates learning in data manipulation and analysis. These hands-on components are designed to reinforce theoretical knowledge with real-world applications.
Who would benefit most from taking this course?
This course is ideal for learners who already have basic programming skills and wish to advance their capabilities in data science and machine learning. It is particularly suited for individuals aspiring to enter roles in data analysis, data engineering, or machine learning, offering skills that are attractive to top technology companies.
How does the depth of this course compare to other similar courses?
This masterclass delves into advanced machine learning algorithms, including sophisticated regularization techniques and unsupervised learning methods like DBSCAN, which are often overlooked in other courses. It provides a balanced approach with both theoretical foundations and practical case studies, positioning it as a competitor to high-priced bootcamps.
What specific tools and platforms does the course cover?
The course includes setup and usage instructions for Anaconda Python and Jupyter, which are essential tools for data science. Additionally, it covers popular libraries such as NumPy for numerical operations, Pandas for data manipulation, and Seaborn for data visualization, providing a comprehensive toolkit for machine learning applications.
What topics are not covered in this course?
While the course is thorough in its coverage of Python-based data science and machine learning, it does not delve into non-Python programming languages or platforms. It also does not cover introductory programming concepts beyond a brief Python Crash Course, focusing instead on advanced applications and techniques within the Python ecosystem.
How much time should I expect to dedicate to this course?
The course consists of 225 lessons, each designed to build progressively on the previous ones. While the total runtime is not specified, students should plan to invest significant time in both the lectures and practical exercises to fully grasp the advanced topics and skills covered in the course.