2021 Python for Data Science & Machine Learning from A-Z

Become a professional Data Scientist and learn how to use NumPy, Pandas, Seaborn, Matplotlib, Machine Learning and more!
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2021 Python for Data Science & Machine Learning from A-Z.

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Course Outline

A comprehensive list of all sections & lectures for this course can be found below.

Introduction to Python for Data Science & Machine Learning from A-Z

Who is this Course for? - 02:44 [Play]

Data Science + Machine Learning Marketplace - 06:56

Data Science Job Opportunities - 04:25

Data Science Job Roles - 10:23

What is a Data Scientist - 17:00

How To Get a Data Science Job - 18:39

Data Science Projects Overview - 11:52

Why We Use Python - 03:15 [Play]

What is Data Science? - 13:24

What is Machine Learning? - 14:22

ML Concepts and Algorithms - 14:43

What is Deep Learning - 11:10

Machine Learning vs Deep Learning - 09:44

What is Python Programming? - 06:04 [Play]

Why Python for Data Science? - 04:36

What is Jupyter? - 03:54

Jupyter Notebook - 18:01

What is Google Colab? - 09:08

Python Variables, Booleans and None - 07:48

Getting Started with Colab - 09:08

Python Operators - 25:27

Python Numbers and Booleans - 07:48

Python Strings - 13:12

Python Conditional Statements - 13:53

Python For Loops and While Loops - 08:08

Python Lists - 05:10

More About Python Lists - 15:09

Python Tuples - 11:25

Python Dictionaries - 20:19

Python Sets - 09:41

Compound Data Types and When to use each Data Type - 12:58

Python Functions - 14:24

Python Object Oriented Programming - 18:48

Intro to Statistics - 07:11 [Play]

Descriptive Statistics - 06:36

Measure of Variability - 12:19

Measure of Variability Continued - 09:36

Measures of Variable Relationship - 07:37

Inferential Statistics - 15:18

Measures of Asymmetry - 01:58

Sampling Distribution - 07:35

What Exactly Probability - 03:45 [Play]

Expected Values - 02:38

Relative Frequency - 05:16

Hypothesis Testing Overview - 09:10

NumPy Array Data Types - 12:59 [Play]

NumPy Arrays - 08:22

NumPy Array Basics - 11:36

NumPy Array Indexing - 09:10

NumPy Array Computations - 05:53

Broadcasting - 04:33

Intro to Pandas - 15:53 [Play]

Intro to Panda Continued - 18:05

Data Visualization Overview - 24:49 [Play]

Different Data Visualization Libraries in Python - 12:49

Python Data Visualization Implementation - 08:27

Intro to Machine Learning - 26:03 [Play]

Exploratory Data Analysis - 13:06 [Play]

Feature Scaling - 07:41 [Play]

Data Cleaning - 07:43

Feature Engineering - 06:11 [Play]

Linear Regression Intro - 08:17 [Play]

Gradient Descent - 05:59

Linear Regression + Correlation Methods - 26:33

Linear Regression Implementation - 05:07

Logistic Regression - 03:23

KNN Overview - 03:01 [Play]

Parametic vs Non-Parametic Models - 03:29

EDA on Iris Dataset - 22:08

KNN - Intuition - 02:17

Implement the KNN algorithm from scratch - 11:45

Compare the Reuslt with Sklearn Library - 03:47

Hyperparameter tuning using the cross-validation - 10:48

The decision boundary visualization - 04:56

KNN - Manhattan vs Euclidean Distance - 11:21

KNN Scaling in KNN - 06:01

Curse of dimensionality - 08:10

KNN use cases - 03:33

KNN pros and cons - 05:33

Decision Trees Section Overview - 04:12 [Play]

EDA on Adult Dataset - 16:54

What is Entropy and Information Gain - 21:51

The Decision Tree ID3 algorithm from scratch Part 1 - 11:33

The Decision Tree ID3 algorithm from scratch Part 2 - 07:35

The Decision Tree ID3 algorithm from scratch Part 3 - 04:07

ID3 - Putting Everything Together - 21:23

Evaluating our ID3 implementation - 16:54

Compare with Sklearn implementation - 08:52

Visualizing the Tree - 10:15

Plot the features importance - 05:52

Decision Trees Hyper-parameters - 11:40

Pruning - 17:11

[Optional] Gain Ration - 02:50

Decision Trees Pros and Cons - 07:32

[Project] Predict whether income exceeds $50Kyr - Overview - 02:33

Ensemble Learning Section Overview - 03:47 [Play]

What is Ensemble Learning? - 13:06

What is Bootstrap Sampling? - 08:26

What is Bagging? - 05:20

Out-of-Bag Error (OOB Error) - 07:47

Implementing Random Forests from scratch Part 1 - 22:34

Implementing Random Forests from scratch Part 2 - 06:11

Compare with sklearn implementation - 03:41

Random Forests Hyper-Parameters - 04:23

Random Forests Pros and Cons - 05:25

What is Boosting? - 04:42

AdaBoost Part 1 - 04:10

AdaBoost Part 2 - 14:34

SVM - Outline - 05:16 [Play]

SVM - SVM intuition - 11:39

SVM - Hard vs Soft Margin - 13:26

C hyper-parameter - 04:18

Kernel Trick - 12:19

SVM - Kernel Types - 18:14

SVM with Linear Dataset (Iris) - 13:35

SVM with Non-linear Dataset - 12:51

SVM with Regression - 05:52

[Project] Voice Gender Recognition using SVM - 04:26

Unsupervised Machine Learning Intro - 20:22 [Play]

Representation of Clusters - 20:49

Data Standardization - 19:05

PCA - Section Overview - 05:13 [Play]

What is PCA? - 09:37

PCA - Drawbacks - 03:32

PCA - Algorithm Steps (Maintenance) - 13:12

Covariance Matrix vs SVD - 04:58

PCA - Main Applications - 02:51

PCA - Image Compression - 27:01

PCA Data Preprocessing - 14:32

PCA - Biplot and the Screen Plot - 17:28

PCA - Feature Scaling and Screen Plot - 09:29

PCA - Supervised vs Unsupervised - 04:56

PCA - Visualization - 07:32

Creating a Data Science Resume - 06:45 [Play]

Data Science Cover Letter - 03:33

How To Contact Recruiters - 04:20

Getting Started with Freelancing - 04:13

Top Freelance Websites - 05:35

Personal Branding - 04:03

Networking Do's and Don'ts - 03:45

Importance of a Website - 02:56

Course Description

Learn Python for Data Science & Machine Learning from A-Z

In this practical, hands-on course you'll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner.

Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job.

We'll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib

NumPy - A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.

Pandas - A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.

NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you would find in Excel or Google Sheets. Blending practical work with solid theoretical training, we take you from the basics of Python Programming for Data Science to mastery.

This Machine Learning with Python course dives into the basics of machine learning using Python. You'll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.

We understand that theory is important to build a solid foundation, we understand that theory alone isn't going to get the job done so that's why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the Python programming language, this course is for you!

Python coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding Python coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.

Together we're going to give you the foundational education that you need to know not just on how to write code in Python, analyze and visualize data and utilize machine learning algorithms but also how to get paid for your newly developed programming skills.

2021 Python for Data Science & Machine Learning from A-Z

All course reviews are written by students who have completed the course or are currently enrolled.

Course Instructor - Juan Galvan

juan galvan
Teaching 12 Courses

juan galvan is currently teaching 12 courses. All courses are currently open for enrollment.

215,500 Enrollments

juan galvan currently has 215,500 global enrollments across 12 courses that are active on the platform.

4.5 Star Rating

juan galvan has an average rating of 4.5/5 stars, across 12 courses.

Hi I'm Juan. I've been an Entrepreneur since grade school. My background is in the tech space from Digital Marketing, E-commerce, Web Development to Programming. I believe in continuous education with the best of a University Degree without all the downsides of burdensome costs and inefficient methods. I look forward to helping you expand your skillsets.



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