Machine Learning – Decision Trees & Random Forests

Course Description:

This excellent Machine Learning – Decision Trees & Random Forests course will teach you two helpful machine learning techniques, decision trees and random forests. If you’re someone who works in analytics, or with big data, this Machine Learning – Decision Trees & Random Forests course will help you to problem solve in a better way. If you’re someone who needs to get to grips with machine learning, this course is for you, and it will help you to learn to use decision trees and random forests.

Our learning material is available to students 24/7 anywhere in the world, so it’s extremely convenient. These intensive online courses are open to everyone, as long as you have an interest in the topic! We provide world-class learning led by IAP, so you can be assured that the material is high quality, accurate and up-to-date.

What skills will I gain?

• 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

What are the requirements?

• You must be 16 or over
• You should have a basic understanding of English, Maths and ICT
• You will need a computer or tablet with internet connection (or access to one)

Meet the Instructor:

Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.

• Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft
• Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too
• Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum
• Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum

Course outline:

• Module 01: Introduction: You, This Course & Us!
• Module 02: Planting the Seed: What Are Decision Trees?
• Module 03: Growing the Tree: Decision Tree Learning
• Module 04: Branching Out: Information Gain
• Module 05: Decision Tree Algorithms
• Module 06: Installing Python: Anaconda & Pip
• Module 07: Back To Basics: Numpy in Python
• Module 08: Back To Basics: Numpy & Scipy in Python
• Module 09: Titanic: Decision Trees Predict Survival (Kaggle) – I
• Module 10: Titanic: Decision Trees Predict Survival (Kaggle) – II
• Module 11: Titanic: Decision Trees Predict Survival (Kaggle) – III
• Module 12: Overfitting: The Bane of Machine Learning
• Module 13: Overfitting Continued
• Module 14: Cross-Validation
• Module 15: Simplicity Is a Virtue: Regularization
• Module 16: The Wisdom Of Crowds: Ensemble Learning
• Module 17: Ensemble Learning Continued: Bagging, Boosting & Stacking
• Module 18: Random Forests: Much More Than Trees
• Module 19: Back On the Titanic: Cross Validation & Random Forests

How will I be assessed?

• You will have one assignment. Pass mark is 65%.
• You will only need to pay £19 for assessment.
• You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days.

What Certification am I going to receive?

Those who successfully pass this course will be awarded a Machine Learning – Decision Trees & Random Forests certificate. Anyone eligible for certification will receive a free e-certificate, and printed certificate.

What careers can I get with this qualification?

Once you have completed this Machine Learning – Decision Trees & Random Forests course you will have desirable skills. You could go on to further study of this topic, or could gain entry level employment in analytics or big data. These roles often command a high salary, for example, the average salary of a Data Scientist in the UK is £43,318, and this will go up with experience (payscale.com). When you complete this Machine Learning – Decision Trees & Random Forests, you could fulfil any of the following roles:

Data Scientist

Big Data Specialist

Data Architect

Data Analyst

COURSE CURRICULUM

1: INTRODUCTION: YOU, THIS COURSE & US!

2: PLANTING THE SEED: WHAT ARE DECISION TREES?

3: GROWING THE TREE: DECISION TREE LEARNING

4: BRANCHING OUT: INFORMATION GAIN

5: DECISION TREE ALGORITHMS

6: INSTALLING PYTHON: ANACONDA & PIP

7: BACK TO BASICS: NUMPY IN PYTHON

8: BACK TO BASICS: NUMPY & SCIPY IN PYTHON

9: TITANIC: DECISION TREES PREDICT SURVIVAL (KAGGLE) – I

10: TITANIC: DECISION TREES PREDICT SURVIVAL (KAGGLE) – II

11: TITANIC: DECISION TREES PREDICT SURVIVAL (KAGGLE) – III

12: OVERFITTING: THE BANE OF MACHINE LEARNING

13: OVERFITTING CONTINUED

14: CROSS-VALIDATION

15: SIMPLICITY IS A VIRTUE: REGULARIZATION

16: THE WISDOM OF CROWDS: ENSEMBLE LEARNING

17: ENSEMBLE LEARNING CONTINUED: BAGGING, BOOSTING & STACKING

18: RANDOM FORESTS: MUCH MORE THAN TREES

19: BACK ON THE TITANIC: CROSS VALIDATION & RANDOM FORESTS