The Premium Machine Learning Artificial Intelligence Super Bundle

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12 Courses & 68 Hours
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What's Included

Machine Learning with Python
  • Certification included
  • Experience level required: Intermediate
  • Access 60 lectures & 9 hours of content 24/7
  • Length of time users can access this course: Lifetime

Course Curriculum

60 Lessons (9h)

  • Your First Program
  • 9. Machine Learning
    Intro to Machine Learning26:03
  • 15. Decision Trees
    15.1 Decision Trees Section Overview4:11
    15.2 EDA on Adult Dataset16:53
    15.3 What is Entropy and Information Gain21:50
    15.4 The Decision Tree ID3 algorithm from scratch Part 111:32
    15.5 The Decision Tree ID3 algorithm from scratch Part 27:35
    15.6 The Decision Tree ID3 algorithm from scratch Part 34:07
    15.7 ID3 - Putting Everything Together21:23
    15.8 Evaluating our ID3 implementation16:53
    15.9 Compare with Sklearn implementation8:51
    15.10 Visualizing the Tree10:15
    15.11 Plot the features importance5:51
    15.12 Decision Trees Hyper-parameters11:39
    15.13 Pruning17:11
    15.14 [Optional] Gain Ration2:49
    15.15 Decision Trees Pros and Cons7:31
    15.16 [Project] Predict whether income exceeds $50Kyr - Overview2:33
  • 16. Ensemble Learning and Random Forests
    Ensemble Learning Section Overview3:46
    What is Ensemble Learning?13:06
    What is Bootstrap Sampling?8:25
    What is Bagging?5:20
    Out-of-Bag Error (OOB Error)7:47
    Implementing Random Forests from scratch Part 122:34
    Implementing Random Forests from scratch Part 26:10
    Compare with sklearn implementation3:41
    Random Forests Hyper-Parameters4:23
    Random Forests Pros and Cons5:25
    What is Boosting?4:41
    AdaBoost Part 14:10
    AdaBoost Part 214:33
  • 17. Support Vector Machines
    SVM - Outline5:15
    SVM - SVM intuition11:38
    SVM - Hard vs Soft Margin13:25
    SVM - C Hyper-Parameter4:17
    SVM - Kernel Trick12:18
    SVM - Kernel Types18:13
    SVM - with Linear Dataset13:35
    SVM - Non-Linear Dataset12:50
    SVM- Multi _ Regression5:51
    SVM - Project Overview (Voice Gender Recognition)4:26
  • 19. PCA
    PCA - Section Overview5:12
    What is PCA9:36
    PCA - Drawbacks3:31
    PCA - Algorithm Steps13:12
    PCA - Covariance Matrix vs SVD4:58
    PCA - Main Applications2:50
    PCA - Image Compression27:00
    PCA - Data Preprocessing14:31
    PCA - BiPlot and The Screen Plot17:27
    PCA - Feature Scaling and Screeplot9:29
    PCA - Supervised vs unsupervised4:55
    PCA - Visualization7:31
  • 20. Data Science Career
    Creating a Data Science Resume6:45
    Data Science Cover Letter3:33
    How to Contact Recruiters4:20
    Getting Started with Freelancing4:13
    Top Freelance Websites5:35
    Personal Branding4:02
    Networking Do's and Don'ts3:45
    Importance of a Website2:56

Machine Learning with Python

Juan Galvan

Juan E. Galvan | Top Instructor | Digital Entrepreneur

4.4/5 Instructor Rating: ★ ★ ★ ★

Juan Galvan has been an Entrepreneur since grade school. He has started several companies, created many products, and sold on various online marketplaces with great success. He founded Sezmi SEO, an agency based out of Seattle, Washington.

His collection of principles, thoughts, and sayings has grown over the years. These have come from the teachings of powerful and famous people like Warren Buffett, Charlie Munger, Peter Drucker, Jim Rohn and his personal mentors.


In this practical, hands-on course, our main objective is to give you the foundational educations of Machine Learning with Python. Understandably, a theory is important to build a solid foundation. However, 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. This section gives you a full introduction to Machine Learning, including Supervised & Unsupervised ML with hands-on, step-by-step training.

  • Access 77 lectures & 12 hours of content 24/7
  • Introduction to Machine learning
  • Understand data processing
  • Learn about linear regression & logistic regression
  • Know what decision trees, ensemble learning, K-nearest neighbors & others are all about
  • Gain insights on support vector machines, PCA & K-means clustering


Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certification of completion included
  • Experience level required: intermediate
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  • Any device with basic specifications


  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.
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