2022 Python for Machine Learning & Data Science Masterclass

Name: 2022 Python for Machine Learning & Data Science Masterclass
Availability: InStock

44h 5m 31s

English

Free

August 27, 2024

2022 Python for Machine Learning & Data Science Masterclass is a 225-lesson 44 hours 5 minutes self-paced course by Udemy. Welcome to the ultimate course on Data Science and Machine Learning using Python!

Course facts

Lessons: 225
Duration: 44 hours 5 minutes
Level: All levels
Language: English
Updated
Instructor: Udemy
Price: Free

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!

Who teaches 2022 Python for Machine Learning & Data Science Masterclass? Udemy

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 2022 Python for Machine Learning & Data Science Masterclass?

0:00 0:00

#	Lesson 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

What courses are similar to 2022 Python for Machine Learning & Data Science Masterclass?

Classic
100 Days of Code: The Complete Python Pro Bootcamp
By: Udemy
Welcome to the 100 Days of Code: The Complete Python Pro Bootcamp , the only course you need to learn to code with Python.
54h 16m4/5
Updated 2y ago
Deep Learning: Advanced Computer Vision
By: Udemy
This is one of the most exciting courses I’ve done and it really shows how fast and how far deep learning has come over the years. When I first started my deep
15h 10m
Updated 2y ago
LeetCode In Python: 50 Algorithms Coding Interview Questions
By: Udemy
In this course, you'll have a detailed, step by step explanation of 50 hand-picked LeetCode questions where you'll learn about the most popular techniques and p
19h 36m
Updated 3y ago
Introduction to Ansible
By: Talk Python Training
A course by Matt Makai of Full Stack Python for software developers who want to learn the Ansible configuration management tool for managing servers.
2h 54m5/5
Updated 2y ago
The Software Architect Mindset (COMPLETE)
By: ArjanCodes (Arjan Egges)
The The Software Architect Mindset course teaches the fundamentals of software architecture and provides practical advice on creating software products that.
12h 6m5/5
Updated 1y ago
Case Study in Product Data Science
By: LunarTech
Unlock your potential in product data science with our comprehensive course designed for aspiring data analysts and product developers.
1h 4m5/5
Updated 1mo ago
Introduction to Inferential Statistics
By: Zero To Mastery
You learn core inferential stats like intervals, tests, ANOVA, and run them in Python. The course shows how to read messy data and make clear data decisions.
9h 25m
Updated 2y ago
Python for Data Science and Machine Learning Bootcamp
By: Udemy
Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python.
24h 49m

More courses by Udemy

New
React - The Complete Guide
React: The Complete Guide by Maximilian Schwarzmüller — original 2022 edition covering React hooks, Redux, Context API, Next.js basics.
47h 42m5/5
Updated 3y ago
Complete C# Unity Game Developer 3D
This is the long-awaited sequel to the Complete Unity Developer - one of the most popular e-learning courses on the internet!
30h 34m
Updated 3y ago
Nest.js Microservices: Build & Deploy a Scaleable Backend
Nest.js is an incredible backend framework that allows us to build scaleable Nodejs backends with very little complexity. A Microservice architecture is a popul
5h 39m5/5
Updated 3y ago
The HTML & CSS Bootcamp 2023 Edition
Brand new HTML & CSS course, just released in February 2023 Check out the promo video to see the beautiful, responsive projects we build in this course!
37h 18m5/5
Updated 3y ago
Microservices with Node JS and React
Event-Based Architecture? Covered! Server side rendering with React? Yep. Scalable, production-ready code? Its here!
54h 13m5/5
FreeClassic
100 Days of Code - The Complete Python Pro Bootcamp for 2023
Watch the 100 Days of Code Python Pro Bootcamp free: 100 daily projects covering Python basics, web scraping, data science, automation and GUI apps.
58h 35m5/5

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.