Byte-Sized-Chunks : Decision Trees and Random Forests

About Course

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  Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

  Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

Topics covered in this course

• Section 1: Decision Fatigue, And Decision Trees
  	Lecture 1	You, This Course, and Us!	01:14
  	Lecture 2	Planting the seed - What are Decision Trees?	17:00
  	Lecture 3	Growing the Tree - Decision Tree Learning	18:03
  	Lecture 4	Branching out - Information Gain	18:51
  	Lecture 5	Decision Tree Algorithms	07:50
  	Lecture 6	Installing Python - Anaconda and Pip	09:00
  	Lecture 7	Back to Basics : Numpy in Python	18:05
  	Lecture 8	Back to Basics : Numpy and Scipy in Python	14:19
  	Lecture 9	Titanic : Decision Trees predict Survival (Kaggle) - I	19:21
  	Lecture 10	Titanic : Decision Trees predict Survival (Kaggle) - II	14:16
  	Lecture 11	Titanic : Decision Trees predict Survival (Kaggle) - III	13:00
  Section 2: A Few Useful Things to Know About Overfitting
  	Lecture 12	Overfitting - The Bane of Machine Learning	19:03
  	Lecture 13	Overfitting Continued	11:19
  	Lecture 14	Cross-Validation	18:55
  	Lecture 15	Simplicity is a virtue - Regularization	07:18
  	Lecture 16	The Wisdom Of Crowds - Ensemble Learning	16:39
  	Lecture 17	Ensemble Learning continued - Bagging, Boosting and Stacking	18:02
  Section 3: Random Forests
  	Lecture 18	Random Forests - Much more than trees	12:28
  	Lecture 19	Back on the Titanic - Cross Validation and Random Forests	20:03

What is covered in this Course?

• • Design and Implement the solution to a famous problem in machine learning: predicting survival probabilities aboard the Titanic
  • Understand the perils of overfitting, and how random forests help overcome this risk
  • Identify the use-cases for Decision Trees as well as Random Forests

Who should buy this course?

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  • Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning
  • Engineers who want to understand or learn machine learning and apply it to problems they are solving
  • Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
  • Tech executives and investors who are interested in big data, machine learning or natural language processing
  • No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to perform the coding exercise and understand the provided source code