Machine Learning

Data is our future. Learn how to not only survive, but thrive in a future dominated by data and Artificial Intelligence. Starting with supervised learning, our courses will take you to unsupervised learning, reinforcement learning, and beyond as you learn how to build intelligent systems for every industry.

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Instructor

Do Hai Minh

Minh1

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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Levels to Mastery

Next class starts Nov 26

4 weeks, 7pm - 9pm, Mon & Wed

5,000,000 VND


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MODULE 1: 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.

REQUIREMENTS

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

Next class starts Dec 3

4 weeks, 7pm - 9pm, Mon & Wed

7,000,000


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MODULE 2: INTRODUCTION TO MODEL OPTIMIZATION AND UNSUPERVISED LEARNING

In this advanced course, we will dive deep into Machine Learning. It is structured around: Model selection: Grid Search, k-fold Cross Validation, Parameter Tuning. Advanced Classification Algorithms: K-NN, SVM, Kernel SVM, Naive Bayes. Unsupervised Learning: K-Means Clustering, Hierarchical Clustering.

REQUIREMENTS

ML Level 1 Completion

Week 1 - Advanced Algorithms 1




Week 2 - Advanced Algorithms 2




Week 3 - Unsupervised Learning 1




Week 4 - Unsupervised Learning 2




What's stopping you?

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