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

Become a professional Data Scientist and learn how to use NumPy, Pandas, Machine Learning and more!

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Last Updated: 2022-05-04 05:05:21 English Course Type: Self Learning

Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant
Learn data cleaning, processing, wrangling and manipulation
How to create resume and land your first job as a Data Scientist
How to use Python for Data Science
How to write complex Python programs for practical industry scenarios
Learn Plotting in Python (graphs, charts, plots, histograms etc)
Learn to use NumPy for Numerical Data
Machine Learning and it's various practical applications
Supervised vs Unsupervised Machine Learning
Learn Regression, Classification, Clustering and Sci-kit learn
Machine Learning Concepts and Algorithms
K-Means Clustering
Use Python to clean, analyze, and visualize data
Building Custom Data Solutions
Statistics for Data Science
Probability and Hypothesis Testing

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’d 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.

The course covers 5 main areas:

1: PYTHON FOR DS+ML COURSE INTRO

This intro section gives you a full introduction to the Python for Data Science and Machine Learning course, data science industry, and marketplace, job opportunities and salaries, and the various data science job roles.

Intro to Data Science + Machine Learning with Python
Data Science Industry and Marketplace
Data Science Job Opportunities
How To Get a Data Science Job
Machine Learning Concepts & Algorithms

2: PYTHON DATA ANALYSIS/VISUALIZATION

This section gives you a full introduction to the Data Analysis and Data Visualization with Python with hands-on step by step training.

Python Crash Course
NumPy Data Analysis
Pandas Data Analysis

3: MATHEMATICS FOR DATA SCIENCE

This section gives you a full introduction to the mathematics for data science such as statistics and probability.

Descriptive Statistics
Measure of Variability
Inferential Statistics
Probability
Hypothesis Testing

4: MACHINE LEARNING

This section gives you a full introduction to Machine Learning including Supervised & Unsupervised ML with hands-on step-by-step training.

Intro to Machine Learning
Data Preprocessing
Linear Regression
Logistic Regression
K-Nearest Neighbors
Decision Trees
Ensemble Learning
Support Vector Machines
K-Means Clustering
PCA

5: STARTING A DATA SCIENCE CAREER

This section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training.

Creating a Resume
Creating a Cover Letter
Personal Branding
Freelancing + Freelance websites
Importance of Having a Website
Networking

By the end of the course you’ll be a professional Data Scientist with Python and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.

Students should have basic computer skills
Students would benefit from having prior Python Experience but not necessary

Students who want to learn about Python for Data Science & Machine Learning

Section 1 : Introduction 7 Lectures 00:36:19
Lecture 1 :
Who is this Course for? Preview
Lecture 2 :
Data Science and Machine Learning Marketplace
Lecture 3 :
Data Science Job Opportunities
Lecture 4 :
Data Science Job Roles
Lecture 5 :
What is a Data Scientist ?
Lecture 6 :
How To Get a Data Science Job
Lecture 7 :
Data Science Projects Overview
Section 2 : Data Science & Machine Learning Concepts 6 Lectures 01:06:35
Lecture 1 :
Why use we python
Lecture 2 :
What is Data Science?
Lecture 3 :
What is Machine Learning?
Lecture 4 :
Machine Learning Concepts & Algorithms
Lecture 5 :
What is Deep Learning?
Lecture 6 :
Machine Learning vs Deep Learning
Section 3 : Python for Data Science 19 Lectures 02:09:39
Lecture 1 :
What is Programming?
Lecture 2 :
Why Python for Data Science?
Lecture 3 :
What is Jupyter?
Lecture 4 :
What is Google Colab?
Lecture 5 :
Python Variables, Booleans and None
Lecture 6 :
Getting Started with Google Colab
Lecture 7 :
Python Operators
Lecture 8 :
Python Numbers & Booleans
Lecture 9 :
Python Strings
Lecture 10 :
Python Conditional Statements
Lecture 11 :
Python For Loops and While Loops
Lecture 12 :
Python Lists
Lecture 13 :
More about Lists
Lecture 14 :
Python Tuples
Lecture 15 :
Python Dictionaries
Lecture 16 :
Python Sets
Lecture 17 :
Compound Data Types & When to use each one?
Lecture 18 :
Python Functions
Lecture 19 :
Object Oriented Programming in Python
Section 4 : Statistics for Data Science 8 Lectures 00:45:14
Lecture 1 :
Intro To Statistics
Lecture 2 :
Descriptive Statistics
Lecture 3 :
Measure of Variability
Lecture 4 :
Measure of Variability Continued
Lecture 5 :
Measures of Variable Relationship
Lecture 6 :
Inferential Statistics
Lecture 7 :
Measure of Asymmetry
Lecture 8 :
Sampling Distribution
Section 5 : Probability and Hypothesis Testing 4 Lectures 00:20:46
Lecture 1 :
What Exactly is Probability?
Lecture 2 :
Expected Values
Lecture 3 :
Relative Frequency
Lecture 4 :
Hypothesis Testing Overview
Section 6 : NumPy Data Analysis 6 Lectures 00:44:09
Lecture 1 :
Intro NumPy Array Data Types
Lecture 2 :
NumPy Arrays
Lecture 3 :
NumPy Arrays Basics
Lecture 4 :
NumPy Array Indexing
Lecture 5 :
NumPy Array Computations
Lecture 6 :
Broadcasting
Section 7 : Pandas Data Analysis 2 Lectures 00:15:52
Lecture 1 :
Introduction to Pandas
Lecture 2 :
Introduction to Pandas Continued
Section 8 : Python Data Visualization 3 Lectures 00:21:15
Lecture 1 :
Data Visualization Overview
Lecture 2 :
Different Data Visualization Libraries in Python
Lecture 3 :
Python Data Visualization Implementation
Section 9 : Machine Learning 1 Lectures 00:00:01
Lecture 1 :
Introduction To Machine Learning
Section 10 : Data Loading and Exploration 1 Lectures 00:13:05
Lecture 1 :
Exploratory Data Analysis
Section 11 : Data Cleaning 2 Lectures 00:15:23
Lecture 1 :
Feature Scaling
Lecture 2 :
Data Cleaning
Section 12 : Feature Selecting and Engineering 1 Lectures 00:06:11
Lecture 1 :
Feature Engineering
Section 13 : Linear and Logistic Regression 5 Lectures 00:22:44
Lecture 1 :
Linear Regression Intro
Lecture 2 :
Gradient Descent
Lecture 3 :
Linear Regression + Correlation Methods
Lecture 4 :
Linear Regression Implementation
Lecture 5 :
Logistic Regression
Section 14 : K Nearest Neighbors 13 Lectures 01:10:47
Lecture 1 :
KNN Overview
Lecture 2 :
parametric vs non-parametric models
Lecture 3 :
EDA on Iris Dataset
Lecture 4 :
The KNN Intuition
Lecture 5 :
Implement the KNN algorithm from scratch
Lecture 6 :
Compare the result with the sklearn library
Lecture 7 :
Hyperparameter tuning using the cross-validation
Lecture 8 :
The decision boundary visualization
Lecture 9 :
Manhattan vs Euclidean Distance
Lecture 10 :
Feature scaling in KNN
Lecture 11 :
Curse of dimensionality
Lecture 12 :
KNN use cases
Lecture 13 :
KNN pros and cons
Section 15 : Decision Trees 16 Lectures 01:16:58
Lecture 1 :
Decision Trees Section Overview
Lecture 2 :
EDA on Adult Dataset
Lecture 3 :
What is Entropy and Information Gain?
Lecture 4 :
The Decision Tree ID3 algorithm from scratch Part 1
Lecture 5 :
The Decision Tree ID3 algorithm from scratch Part 2
Lecture 6 :
The Decision Tree ID3 algorithm from scratch Part 3
Lecture 7 :
ID3 - Putting Everything Together
Lecture 8 :
Evaluating our ID3 implementation
Lecture 9 :
Compare with Sklearn implementation
Lecture 10 :
Visualizing the tree
Lecture 11 :
Plot the features importance
Lecture 12 :
Decision Trees Hyper-parameters
Lecture 13 :
Pruning
Lecture 14 :
[Optional] Gain Ration
Lecture 15 :
Decision Trees Pros and Cons
Lecture 16 :
[Project] Predict whether income exceeds $50K/yr - Overview
Section 16 : Ensemble Learning and Random Forests 13 Lectures 01:21:28
Lecture 1 :
Ensemble Learning Section Overview
Lecture 2 :
What is Ensemble Learning?
Lecture 3 :
What is Bootstrap Sampling?
Lecture 4 :
What is Bagging?
Lecture 5 :
Out-of-Bag Error (OOB Error)
Lecture 6 :
Implementing Random Forests from scratch Part 1
Lecture 7 :
Implementing Random Forests from scratch Part 2
Lecture 8 :
Compare with sklearn implementation
Lecture 9 :
Random Forests Hyper-Parameters
Lecture 10 :
Random Forests Pros and Cons
Lecture 11 :
What is Boosting?
Lecture 12 :
AdaBoost Part 1
Lecture 13 :
AdaBoost Part 2
Section 17 : Support Vector Machines 10 Lectures 01:23:37
Lecture 1 :
SVM Outline
Lecture 2 :
SVM intuition
Lecture 3 :
Hard vs Soft Margins
Lecture 4 :
C hyper-parameter
Lecture 5 :
Kernel Trick
Lecture 6 :
SVM - Kernel Types
Lecture 7 :
SVM with Linear Dataset (Iris)
Lecture 8 :
SVM with Non-linear Dataset
Lecture 9 :
SVM with Regression
Lecture 10 :
[Project] Voice Gender Recognition using SVM
Section 18 : K- Means 3 Lectures 00:00:03
Lecture 1 :
Unsupervised Machine Learning Intro
Lecture 2 :
Unsupervised Machine Learning Continued
Lecture 3 :
Data Standardization
Section 19 : PCA 12 Lectures 01:15:47
Lecture 1 :
PCA Section Overview
Lecture 2 :
What is PCA?
Lecture 3 :
PCA Drawbacks
Lecture 4 :
PCA Algorithm Steps (Mathematics)
Lecture 5 :
Covariance Matrix vs SVD
Lecture 6 :
PCA - Main Applications
Lecture 7 :
PCA - Image Compression
Lecture 8 :
PCA Data Preprocessing
Lecture 9 :
PCA - Biplot and the Screen Plot
Lecture 10 :
PCA - Feature Scaling and Screen Plot
Lecture 11 :
PCA - Supervised vs Unsupervised
Lecture 12 :
PCA - Visualization
Section 20 : Data Science Career 8 Lectures 00:35:09
Lecture 1 :
Creating A Data Science Resume
Lecture 2 :
Data Science Cover Letter
Lecture 3 :
How to Contact Recruiters
Lecture 4 :
Getting Started with Freelancing
Lecture 5 :
Top Freelance Websites
Lecture 6 :
Personal Branding
Lecture 7 :
Networking Do's and Don'ts
Lecture 8 :
Importance of a Website

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