Machine Learning with Python

As part of this initiative, we'll be producing a variety of videos on different technical topics. The videos below are created by a group of people from around the world, some are from high school, while some in grad school or industry. This is an experimental initiative, and we plan to constantly improve and add more topics.

Practical Machine Learning with Python

- Team (# of "thank you" received*): Gabriel Chuan (8), Ravi Teja Chunduru (7), Madhav Datt (8), Isha Gogia (9), Venkata Karthik Gullapalli (16), Ashley Huynh (4), Varun Joshi (2), Rohan Kapur (32), Lenny Khazan (8), Pratyush Kulwal (5), Abhijit Kumar (9), Margaret LaBelle (37), Edwin Limanara (22), Hazem Nomerh (7), Shweta Oak (8), Afelio Padilla (58), Suraj Pai (21), Eduardo Blancas Reyes (19), Mostafa Samir (9), Damini Satya (21), Kewal Shah (13), Tannishk Sharma (13), Tejas Sarma (23), Shreyas Krishnan Shrikanth (31), Koo Ping Shung (9), Sudheesh Singanamalla (13), Max Smith (12) --- from Egypt, India, Singapore, Spain and USA.
- Video Team: Isha Gogia, Ashley Huynh, Rohan Kapur, Margaret LaBelle, Afelio Padilla, Suraj Pai, Damini Satya, Sudheesh Singanamalla - Helped create videos for the course
- Practical Examples Team: Rohan Kapur, Eduardo Blancas Reyes, Mostafa Samir, Tannishk Sharma, Max Smith - ML experts who helped analyze, code and present practical examples for the course.
- Admin Team: Edwin Limanara, Tejas Sarma - Helped with logistical aspects
- Directly Responsible Individuals (DRI) Team: Rohan Kapur, Eduardo Blancas Reyes - Topic experts who helped set and maintain the direction for the course
- Published on: 05/18/2016.
- Course built in: 3 weeks - from syllabus creation to video production, assignment design and practical examples.
- Statistics: 26 modules, 6 chapters, 22 assignments, 8 practical examples
- Feedback: Please feel free to give your constructive feedback for this course below (link).

  • Chapter 1 - Introduction to ML Software Stack
  • Chapter 2 - Introduction to Machine Learning
  • Chapter 3 - Working with Data
  • Chapter 4 - Regression Algorithms
  • Chapter 5 - Classification Algorithms
  • Chapter 6 - Unsupervised Learning Algorithms (clustering)
  • Chapter 7 - Practical Methodologies
  • Practical Examples - Try them yourself!


  • Chapter 1 - Introduction to Machine Learning Software Stack







    Chapter 2 - Introduction to Machine Learning



    Assignment 2.1


    Assignment 2.2


    Assignment 2.3


    Assignment 2.4


    Assignment 2.5


    Assignment 2.6





    Chapter 3 - Working with Data


    Assignment 3.1


    Assignment 3.2




    Chapter 4 - Regression Algorithms


    - Predicting Sales Amount Using Money Spent on Ads - Practical Example by Mostafa Samir.
    - Assignment 4.1


    - Which is more important, TV or Newspaper Ads? - Practical Example by Mostafa Samir.
    - Assignment 4.2


    - Optimizing the Models - Practical Example by Mostafa Samir.
    - Assignment 4.3




    Chapter 5 - Classification Algorithms


    Assignment 5.1


    - Classifying Malignant/Benign Breast Tumors with Logistic Regression - Practical Example by Mostafa Samir.
    - Assignment 5.2


    - Visualizing SVMs with Pima Indians Diabetes Dataset - Practical Example by Mostafa Samir.
    - Assignment 5.3


    Assignment 5.4


    Classification of Iris flowers based on Sepal Length, Sepal Width, Petal Length and Petal Width - Practical Example by Tannishk Sharma.




    Chapter 6 - Unsupervised Learning Algorithms (clustering)


    Assignment 6.1


    Assignment 6.2


    - K Nearest Neighbor - Practical Example by Max Smith.
    - Assignment 6.3



    - Finding Similar States - Clustering using Scikit-learn and Pandas - Practical Example by Eduardo Blancas Reyes.
    - Assignment 6.4 A



    Assignment 6.4 B


    Assignment 6.5




    Chapter 7 - Practical Methodologies


    Assignment 7.3


    Assignment 7.4


    Assignment 7.5


    Assignment 7.6


    (Updated on 8/2/2016) Assignment 7.7






    Practical Examples - Try them yourself!


    - Chapter 4 - Regression Algorithms Predicting Sales Amount Using Money Spent on Ads by Mostafa Samir.
    - Chapter 4 - Regression Algorithms Which is more important, TV or Newspaper Ads? by Mostafa Samir.
    - Chapter 4 - Regression Algorithms Optimizing the Models by Mostafa Samir.
    - Chapter 5 - Classification Algorithms Classifying Malignant/Benign Breast Tumors with Logistic Regression by Mostafa Samir.
    - Chapter 5 - Classification Algorithms Visualizing SVMs with Pima Indians Diabetes Dataset by Mostafa Samir.
    - Chapter 5 - Classification Algorithms Classification of Iris flowers based on Sepal Length, Sepal Width, Petal Length and Petal Width by Tannishk Sharma.
    - Chapter 6 - Clustering K Nearest Neighbor by Max Smith.
    - Chapter 6 - Clustering Finding Similar States - Clustering using Scikit-learn and Pandas by Eduardo Blancas Reyes.
    - Chapter 7 - Practical Methodologies Evaluating Estimator performance using cross-validation by Afelio Padilla.
    - Other Examples (Github link):
    -- Learning Curves and Bias-Variance Tradeoff by Mostafa Samir and Afelio Padilla.
    -- Ng's Machine Learning Exercise 6 - Support Vector Machines by Afelio Padilla.
    -- Iris PCA by Afelio Padilla.
    -- Error Analysis and Classification Measures by Afelio Padilla.
    -- Classifying Malignant/Benign Breast Tumors with Artificial Neural Networks by Rohan Kapur.



    * = # of "thank you" represents the number of acknowledgement each person received from another, since the beginning of this initiative. Though not completely representative of one's effort, it gives a general idea of everyone's contribution level. Note1: Some people joined the course late, but made huge contribution, so their "# of thank you's" are less. Note2: Some people helped with previous courses, and have been consistent, so their "# of thank you's" are more.