CF

Python for Data Science and Machine Learning Bootcamp

24h 49m 42s
English
Paid
Updated September 2026

Python for Data Science and Machine Learning Bootcamp is a 152-lesson 24 hours 49 minutes self-paced course by Udemy. Data science pulls together several skills at once: programming, statistics, visualization, and machine learning.

Course facts

Lessons
152
Duration
24 hours 49 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
Udemy
Price
Premium

Data science pulls together several skills at once: programming, statistics, visualization, and machine learning. This bootcamp covers that full stack in Python rather than teaching each piece in isolation.

What the course walks through

  • Core Python programming and the NumPy library for numerical work
  • Cleaning and reshaping data with pandas, including Excel files and SQL connections
  • Building visualizations with Matplotlib, Seaborn, and interactive plots in Plotly
  • Pulling data from the web through scraping
  • Applying Scikit-Learn machine learning models, starting with linear regression

Who this suits

It's aimed at two groups: people with some prior programming exposure who want a structured path into data science, and developers already comfortable coding who want to pivot toward machine learning work.

Every lecture pairs with a code notebook, so you're working alongside real examples rather than just watching slides.

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!

We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:

  • Programming with Python
  • NumPy with Python
  • Using pandas Data Frames to solve complex tasks
  • Use pandas to handle Excel Files
  • Web scraping with python
  • Connect Python to SQL
  • Use matplotlib and seaborn for data visualizations
  • Use plotly for interactive visualizations
  • Machine Learning with SciKit Learn, including:
  • Linear Regression
  • K Nearest Neighbors
  • K Means Clustering
  • Decision Trees
  • Random Forests
  • Natural Language Processing
  • Neural Nets and Deep Learning
  • Support Vector Machines
  • and much, much more!

Enroll in the course and become a data scientist today!

Requirements:
  • Some programming experience
  • Admin permissions to download files
Who this course is for:
  • This course is meant for people with at least some programming experience

What you'll learn:

  • Use Python for Data Science and Machine Learning
  • Use Spark for Big Data Analysis
  • Implement Machine Learning Algorithms
  • Learn to use NumPy for Numerical Data
  • Learn to use Pandas for Data Analysis
  • Learn to use Matplotlib for Python Plotting
  • Learn to use Seaborn for statistical plots
  • Use Plotly for interactive dynamic visualizations
  • Use SciKit-Learn for Machine Learning Tasks
  • K-Means Clustering
  • Logistic Regression
  • Linear Regression
  • Random Forest and Decision Trees
  • Natural Language Processing and Spam Filters
  • Neural Networks
  • Support Vector Machines

Who teaches Python for Data Science and Machine Learning Bootcamp? Udemy

Udemy thumbnail

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.

What lessons are included in Python for Data Science and Machine Learning Bootcamp?

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#1: Introduction to the Course
All Course Lessons (152)
#Lesson TitleDurationAccess
1
Introduction to the Course Demo
03:34
2
Course Help and Welcome
00:37
3
Python Environment Setup
11:15
4
Jupyter Notebooks
13:49
5
Optional: Virtual Environments
09:52
6
Welcome to the Python Crash Course Section!
00:18
7
Introduction to Python Crash Course
01:27
8
Python Crash Course - Part 1
19:31
9
Python Crash Course - Part 2
15:15
10
Python Crash Course - Part 3
16:40
11
Python Crash Course - Part 4
15:38
12
Python Crash Course Exercises - Overview
03:36
13
Python Crash Course Exercises - Solutions
11:57
14
Welcome to the NumPy Section!
00:12
15
Introduction to Numpy
02:14
16
Numpy Arrays
16:51
17
Numpy Array Indexing
18:24
18
Numpy Operations
07:05
19
Numpy Exercises Overview
02:47
20
Numpy Exercises Solutions
15:33
21
Welcome to the Pandas Section!
00:15
22
Introduction to Pandas
01:45
23
Series
10:40
24
DataFrames - Part 1
15:32
25
DataFrames - Part 2
17:11
26
DataFrames - Part 3
09:13
27
Missing Data
06:20
28
Groupby
06:50
29
Merging Joining and Concatenating
08:57
30
Operations
12:05
31
Data Input and Output
14:01
32
SF Salaries Exercise Overview
01:56
33
SF Salaries Solutions
15:27
34
Ecommerce Purchases Exercise Overview
02:12
35
Ecommerce Purchases Exercise Solutions
15:14
36
Welcome to the Data Visualization Section!
00:23
37
Introduction to Matplotlib
03:03
38
Matplotlib Part 1
16:59
39
Matplotlib Part 2
15:52
40
Matplotlib Part 3
11:53
41
Matplotlib Exercises Overview
01:48
42
Matplotlib Exercises - Solutions
10:20
43
Introduction to Seaborn
02:59
44
Distribution Plots
18:22
45
Categorical Plots
17:19
46
Matrix Plots
10:15
47
Grids
08:31
48
Regression Plots
07:15
49
Style and Color
08:22
50
Seaborn Exercise Overview
01:54
51
Seaborn Exercise Solutions
07:09
52
Pandas Built-in Data Visualization
13:28
53
Pandas Data Visualization Exercise
01:24
54
Pandas Data Visualization Exercise- Solutions
08:56
55
Introduction to Plotly and Cufflinks
03:23
56
Plotly and Cufflinks
18:39
57
Introduction to Geographical Plotting
00:59
58
Choropleth Maps - Part 1 - USA
19:27
59
Choropleth Maps - Part 2 - World
06:54
60
Choropleth Exercises
03:13
61
Choropleth Exercises - Solutions
10:02
62
Welcome to the Data Capstone Projects!
00:18
63
911 Calls Project Overview
02:08
64
911 Calls Solutions - Part 1
14:30
65
911 Calls Solutions - Part 2
17:38
66
Finance Data Project Overview
03:07
67
Finance Project - Solutions Part 1
16:14
68
Finance Project - Solutions Part 2
18:12
69
Finance Project - Solutions Part 3
06:25
70
Welcome to the Machine Learning Section!
00:32
71
Supervised Learning Overview
08:22
72
Evaluating Performance - Classification Error Metrics
16:38
73
Evaluating Performance - Regression Error Metrics
05:37
74
Machine Learning with Python
09:28
75
Linear Regression Theory
04:34
76
Linear Regression with Python - Part 1
18:17
77
Linear Regression with Python - Part 2
07:06
78
Linear Regression Project Overview
02:32
79
Linear Regression Project Solution
18:44
80
Bias Variance Trade-Off
06:26
81
Logistic Regression Theory
11:54
82
Logistic Regression with Python - Part 1
17:44
83
Logistic Regression with Python - Part 2
16:58
84
Logistic Regression with Python - Part 3
08:16
85
Logistic Regression Project Overview
01:37
86
Logistic Regression Project Solutions
11:06
87
KNN Theory
05:40
88
KNN with Python
19:40
89
KNN Project Overview
01:13
90
KNN Project Solutions
14:15
91
Introduction to Tree Methods
06:54
92
Decision Trees and Random Forest with Python
13:58
93
Decision Trees and Random Forest Project Overview
03:11
94
Decision Trees and Random Forest Solutions Part 1
12:15
95
Decision Trees and Random Forest Solutions Part 2
08:47
96
SVM Theory
04:37
97
Support Vector Machines with Python
17:53
98
SVM Project Overview
02:22
99
SVM Project Solutions
10:10
100
K Means Algorithm Theory
05:16
101
K Means with Python
12:36
102
K Means Project Overview
02:54
103
K Means Project Solutions
16:39
104
Principal Component Analysis
03:27
105
PCA with Python
17:00
106
Recommender Systems
04:14
107
Recommender Systems with Python - Part 1
13:38
108
Recommender Systems with Python - Part 2
13:22
109
Natural Language Processing Theory
05:08
110
NLP with Python - Part 1
16:03
111
NLP with Python - Part 2
18:48
112
NLP with Python - Part 3
17:31
113
NLP Project Overview
02:05
114
NLP Project Solutions
19:27
115
Welcome to the Deep Learning Section!
00:22
116
Introduction to Artificial Neural Networks (ANN)
02:16
117
Perceptron Model
10:40
118
Neural Networks
07:20
119
Activation Functions
10:40
120
Multi-Class Classification Considerations
10:35
121
Cost Functions and Gradient Descent
18:14
122
Backpropagation
14:48
123
TensorFlow vs Keras
02:14
124
TF Syntax Basics - Part One - Preparing the Data
10:50
125
TF Syntax Basics - Part Two - Creating and Training the Model
14:00
126
TF Syntax Basics - Part Three - Model Evaluation
12:57
127
TF Regression Code Along - Exploratory Data Analysis
18:51
128
TF Regression Code Along - Exploratory Data Analysis - Continued
13:16
129
TF Regression Code Along - Data Preprocessing and Creating a Model
08:43
130
TF Regression Code Along - Model Evaluation and Predictions
11:24
131
TF Classification Code Along - EDA and Preprocessing
08:06
132
TF Classification - Dealing with Overfitting and Evaluation
16:51
133
TensorFlow 2.0 Project Options Overview
01:41
134
TensorFlow 2.0 Project Notebook Overview
07:42
135
Keras Project Solutions - Dealing with Missing Data
20:36
136
Keras Project Solutions - Dealing with Missing Data - Part Two
14:47
137
Keras Project Solutions - Categorical Data
12:03
138
Keras Project Solutions - Data PreProcessing
17:24
139
Keras Project Solutions - Data PreProcessing
03:46
140
Keras Project Solutions - Creating and Training a Model
03:58
141
Keras Project Solutions - Model Evaluation
09:43
142
Tensorboard
18:23
143
Welcome to the Big Data Section!
00:24
144
Big Data Overview
05:32
145
Spark Overview
09:01
146
AWS Account Set-Up
04:14
147
EC2 Instance Set-Up
16:19
148
SSH with Mac or Linux
04:50
149
PySpark Setup
23:49
150
Lambda Expressions Review
05:27
151
Introduction to Spark and Python
08:18
152
RDD Transformations and Actions
23:10
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Frequently asked questions

What are the prerequisites for this course?
This course does not require prior experience in data science or machine learning. It includes a Python crash course that covers the basics of Python programming, making it accessible to beginners. However, familiarity with basic programming concepts can be beneficial.
What kind of projects will I work on during the course?
The course includes practical exercises such as the SF Salaries and Ecommerce Purchases exercises, which help students apply learned concepts in real-world scenarios. Additionally, students will engage in data visualization projects using Matplotlib and Seaborn, and create geographical plots using Plotly and Cufflinks.
Who is the target audience for this course?
The course is designed for individuals interested in pursuing a career in data science and machine learning. It caters to beginners who want to learn Python for data analysis and those seeking to enhance their skills in using machine learning algorithms for data-driven decision-making.
How does this course compare in depth with other data science courses?
With 152 lessons, this course provides a comprehensive introduction to Python for data science, covering essential libraries like NumPy, Pandas, Matplotlib, and Seaborn. It also introduces students to machine learning concepts, offering a balance between foundational skills and practical applications.
What specific tools and platforms does the course cover?
The course extensively covers Python libraries such as NumPy and Pandas for data manipulation, Matplotlib and Seaborn for data visualization, and Plotly for creating interactive plots. It also introduces students to Jupyter Notebooks as a platform for writing and running Python code.
What topics are not covered in this course?
While the course covers foundational data science and machine learning topics, it does not delve into advanced machine learning techniques or cover deep learning frameworks such as TensorFlow or PyTorch. It focuses primarily on Python and its data science libraries.
How much time should I expect to commit to this course?
The course consists of 152 lessons. While the exact runtime is not specified, students should be prepared to dedicate several hours per week to engage with video lectures, complete exercises, and review provided materials to gain a solid understanding of the subject matter.