Introduction to Supervised Learning using Regression and Random Forests

A fast-paced introduction to using Python for basic machine learning. Topics covered are decision trees, linear/logistic regression, and random forest classifiers - all the basics you need to know to set up your first machine learning models.

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Start day

Dec 27

4 weeks

7 - 9pm


5,000,000 VND

Student Discount Available

Copac Square

G - floor, 12 Ton Dan, District 4


Do Hai Minh


A goal-oriented, ambitious programmer with strong analytical technical background of education & practices.

Proactive, hard-working in nature and delicate for quality of solution and performance. Finding passion & motivation in challenges relating to creative problem-solvers

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Graduates will understand the basics of using Python and its libraries including Pandas and Scikit-Learn to analyze data and build basic classifiers. The capstone project is to perform image recognition on the MNIST image dataset. By completing the course, graduates will be qualified to take the following 4 weeks Machine Learning Level 2 Course.


Anyone with a knowledge of how to operate a computer and search for things on Google. Programming knowledge is always encouraged, but not necessary. For extra reference, feel free to investigate a few articles online about programming (your specific course will offer specific pre-material).

Week 1 - Exploring Data

Lecture: Python Intro. What is Data Science?

Lab: Exploring Breast Cancer

Assignment: Predicting Survival on the Titanic

Week 2 - Linear Regression

Lecture: Explaining Linear Regression

Lab: Exploring Restaurant Reviews

Assignment: Predicting Stock Prices

Week 3 - Logistic Regression

Lecture: Logistic Regression

Lab: Sentiment Analysis - Deploy your model

Assignment: Movie Review Analysis

Week 4 - Random Forests

Lecture: Build Your First Classifiers

Lab: Random Forests Example

Assignment: Final Project



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