Machine Learning - Fun and Easy using Python and Keras

Ritesh Kanjee, Masters Degree in Electronic Engineering

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43 Videos (5h 6m)
    • Section 1 Lecture 1 - Introduction

      2:14
    • Section 2 Lecture 2 - Download and Install Python Anaconda Distribution

      9:23
    • Section 2 Lecture 3 (Part 1) - "Hello World" in Jupyter Notebook

      2:43
    • Section 2 Lecture 3 (Part 2) - "Hello World" in Jupyter Notebook

      12:00
    • Section 2 Lecture 4 - Installation for Mac Users

      3:19
    • Section 4 Lecture 7 - Linear Regression - Theory

      7:27
    • Section 4 Lecture 8: Linear Regression - Practical Labs

      10:13
    • Section 5 Lecture 9 - Decision Tree - Theory

      8:20
    • Section 5 Lecture 10 - Decision Tree - Practical Labs

      10:39
    • Section 6 Lecture 11- Random Forest - Theory

      7:14
    • Section 6 Lecture 12: Random Forest Practical Labs

      8:03
    • Section 8 Lecture 14 - Logistic Regression - Theory

      7:43
    • Section 8 Lecture 15 - Logistic Regression Classification - Practical Labs

      6:58
    • Section 9 Lecture 16 - K -Nearest Neighbors - Theory

      5:45
    • Section 9 Lecture 17 - KNN Classification - Practical Labs

      6:46
    • Section 10 Lecture 18 - Support Vector Machine -Theory

      7:27
    • Section 10 Lecture 19 - Linear SVM - Practical Labs

      2:54
    • Section 10 Lecture 20 - Non Linear SVM - Practical Labs

      1:53
    • Section 11 Lecture 21 - Naive Bayes - Theory

      11:40
    • Section 11 Lecture 22 - Naive Bayes - Practical Labs

      6:05
    • Section 13 Lecture 24 - K - Means Clustering

      8:42
    • Section 13 Lecture 25 - K - Means Clustering - Practical Labs Part A

      6:56
    • Section 13 Lecture 26 - K - Means Clustering - Practical Labs Part B

      3:45
    • Section 14 Lecture 27 - Hierarchical Clustering - Theory

      9:32
    • Section 14 Lecture 28 - Hierarchical clustering - Practical Labs

      0:36
    • Section 14 Lecture 29 - Review Lecture

      8:07
    • Section 14 Lecture 31 - Apriori

      12:30
    • Section 16 Lecture 32 - Apriori - Practical Labs

      8:23
    • Sections 16 Lecture 33 - Eclat - Theory

      5:44
    • Section 16 Lecture 34 - Eclat Practical Labs

      6:53
    • Section 18 Lecture 36 - Principal Component Analysis - Theory

      12:48
    • Section 18 Lecture 37 - PCA - Practical Labs

      3:21
    • Section 19 Lecture 38 - Linear Discriminant Analysis - Theory

      7:40
    • Section 19 Lecture 39 - Linear Discriminant Analysis LDA - Practical Labs

      5:17
    • Section 21 Lecture 41 - Artificial Neural Networks - Theory

      18:30
    • Section 21 Lecture 42 - ANN-perceptron - Practical Labs A

      3:56
    • Section 21 Lecture 43 - ANN MLC - Practical Labs_C

      2:57
    • Section 21 Lecture 44 - ANN MLC - Practical Labs_C

      4:07
    • Section 22 Lecture 45 - Convolutional Neural Networks - Theory

      11:18
    • Section 22 Lecture 46 - Convolution Neural Networks - Practical Labs

      8:09
    • Section 23 Lecture 47 Recurrent Neural Networks Theory

      12:03
    • Section 23 Lecture 48 Recurrent Neural Networks Practical Labs

      5:25
    • Section 24 Lecture 49 - Conclusion

      0:59

About This Class

Welcome to the Fun and Easy Machine learning Course in Python and Keras.

Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science. 

So Many Machine Learning Courses Out There, Why This One?

This is a valid question and the answer is simple. This is the ONLY course on Udemy which will get you implementing some of the most common machine learning algorithms on real data in Python. Plus, you will gain exposure to neural networks (using the H2o framework) and some of the most common deep learning algorithms with the Keras package. 

We designed this course for anyone who wants to learn the state of the art in Machine learning in a simple and fun way without learning complex math or boring explanations.  Each theoretically lecture is uniquely designed using whiteboard animations which can maximize engagement in the lectures and improves knowledge retention. This ensures that you absorb more content than you would traditionally would watching other theoretical videos and or books on this subject.

What you will Learn in this Course

This is how the course is structured:

  • Regression – Linear Regression, Decision Trees, Random Forest Regression,
  • Classification – Logistic Regression, K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naive Bayes,
  • Clustering - K-Means, Hierarchical Clustering,
  • Association Rule Learning - Apriori, Eclat,
  • Dimensionality Reduction - Principle Component Analysis, Linear Discriminant  Analysis,
  • Neural Networks - Artificial Neural Networks, Convolution Neural Networks, Recurrent Neural Networks.

 

Excited Yet?

So as you can see you are going to be learning to build a lot of impressive Machine Learning apps in this 3 hour course. The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today. Start analyzing  data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual examples of your  machine learning abilities.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different  techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. 

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Ritesh Kanjee

Masters Degree in Electronic Engineering

Arduino Startups has over 7 years in Printed Circuit Board (PCB) design as well in image processing and embedded control.

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