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

Become a professional Data Scientist and learn Machine Learning, Data Analysis + Visualization, Web Apps + more!

20 sections • 141 lectures • 23hrs 13mins
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Learn Python for Data Science & Machine Learning from A-Z
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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:
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

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
  • Matplotlib
  • Seaborn
  • Plotly

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

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

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.

Course content
20 sections • 141 lectures • 23hrs 13mins

Who is this Course for - 02mins
DS + ML Marketplace - 06mins
Data Science Job Opportunities - 04mins
Data Science Job Roles - 10mins
What is a Data Scientist - 17mins
How To Get a Data Science Job - 18mins
Data Science Projects Overview - 11mins
Why We Use Python - 03mins
What is Data Science - 13mins
What is Machine Learning - 14mins
ML Concepts & Algorithms - 14mins
Machine Learning vs Deep Learning - 11mins
What is Deep Learning - 09mins
What is Python Programming - 06mins
Why Python for Data Science - 04mins
What is Jupyter - 03mins
What is Colab - 03mins
Jupyter Notebook - 18mins
Getting Started with Colab - 09mins
Python Variables, Booleans and None - 11mins
Python Operators - 25mins
Compound Data Types and When to use each Data Type - 12mins
Python Numbers and Booleans - 07mins
Python Strings - 13mins
Python Conditional Statements - 13mins
Python For Loops and While Loops - 08mins
Python Lists - 05mins
More About Python Lists - 15mins
Python Tuples - 11mins
Python Dictionaries - 20mins
Python Sets - 09mins
Functions - 14mins
Python Object Oriented Programming - 18mins
Intro to Statistics - 07mins
Descriptive Statistics - 06mins
Measure of Variability - 12mins
Measure of Variability Continued - 09mins
Measures of Variable Relationship - 07mins
Inferential Statistics - 15mins
Measures of Asymmetry - 01mins
Sampling Distribution - 07mins
What Exactly Probability - 03mins
Expected Values - 02mins
Relative Frequency - 05mins
Hypothesis Testing Overview - 09mins
NumPy Array Data Types - 12mins
NumPy Arrays - 08mins
NumPy Array Basics - 11mins
NumPy Array Indexing - 09mins
NumPy Array Computations - 05mins
Broadcasting - 04mins
Intro to Pandas - 15mins
Intro to Panda Continued - 18mins
Data Visualization Overview - 24mins
Different Data Visualization Libraries in Python - 12mins
Python Data Visualization Implementation - 08mins
Intro to ML - 26mins
Exploratory Data Analysis - 13mins
Feature Scaling - 07mins
Data Cleaning - 07mins
Feature Engineering - 06mins
Linear Regression Intro - 08mins
Gradient Descent - 05mins
Linear Regression + Correlation Methods - 26mins
Linear Regression Implementation - 05mins
Logistic Regression - 03mins
KNN Overview - 03mins
Parametic vs Non-Parametic Models - 03mins
EDA on Iris Dataset - 22mins
KNN - Intuition - 02mins
Implement the KNN algorithm from scratch - 11mins
Compare the Result with Sklearn Library - 03mins
KNN Hyperparameter tuning using the cross-validation - 10mins
The decision boundary visualization - 04mins
KNN - Manhattan vs Euclidean Distance - 11mins
KNN Scaling in KNN - 06mins
Curse of dimensionality - 08mins
KNN use cases - 03mins
KNN pros and cons - 05mins
Decision Trees Section Overview - 04mins
EDA on Adult Dataset - 16mins
What is Entropy and Information Gain - 21mins
The Decision Tree ID3 algorithm from scratch Part 1 - 11mins
The Decision Tree ID3 algorithm from scratch Part 2 - 07mins
The Decision Tree ID3 algorithm from scratch Part 3 - 04mins
ID3 - Putting Everything Together - 21mins
Evaluating our ID3 implementation - 16mins
Compare with Sklearn implementation - 08mins
Visualizing the Tree - 10mins
Plot the features importance - 05mins
Decision Trees Hyper-parameters - 11mins
Pruning - 17mins
[Optional] Gain Ration - 02mins
Decision Trees Pros and Cons - 07mins
[Project] Predict whether income exceeds $50Kyr - Overview - 02mins
Ensemble Learning Section Overview - 03mins
What is Ensemble Learning - 13mins
What is Bootstrap Sampling - 08mins
What is Bagging - 05mins
Out-of-Bag Error - 07mins
Implementing Random Forests from scratch Part 1 - 22mins
Implementing Random Forests from scratch Part 2 - 06mins
Compare with sklearn implementation - 03mins
Random Forests Hyper-Parameters - 04mins
Random Forests Pros and Cons - 05mins
What is Boosting - 04mins
AdaBoost Part 1 - 04mins
AdaBoost Part 2 - 14mins
SVM - Outline - 05mins
SVM - SVM intuition - 11mins
SVM - Hard vs Soft Margin - 13mins
SVM - C HP - 04mins
SVM - Kernel Trick - 12mins
SVM - Kernel Types - 18mins
SVM - Linear Dataset - 13mins
SVM - Non-Linear Dataset - 12mins
SVM with Regression - 05mins
SVM - Project Overview - 04mins
Unsupervised Machine Learning Intro - 20mins
Representation of Clusters - 20mins
Data Standardization - 19mins
PCA - Section Overview - 05mins
What is PCA - 09mins
PCA - Drawbacks - 03mins
PCA - Algorithm Steps - 13mins
PCA - Cov vs SVD - 04mins
PCA - Main Applications - 02mins
PCA - Image Compression Scratch - 27mins
PCA - Data Preprocessing Scratch - 14mins
PCA - BiPlot - 17mins
PCA - Feature Scaling and Screeplot - 09mins
PCA - Supervised vs unsupervised - 04mins
PCA - Visualization - 07mins
Creating a Data Science Resume - 06mins
Data Science Cover Letter - 03mins
How To Contact Recruiters - 04mins
Getting Started with Freelancing - 04mins
Top Freelance Websites - 05mins
Personal Branding - 04mins
Networking Do's and Don'ts - 03mins
Importance of a Website - 02mins

What you'll learn/Goals

  • 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
Juan E. Galvan

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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Sunil Tiwari Bishwakarma
This course needs an update.
14-Sep-2023 10:50:29 PM

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