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Course curriculum
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1
Introduction
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2
What is ML?
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3
Support Vector Machines (SVMs)
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4
Decision Trees
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5
Overfitting - the Bane of Machine Learning
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6
Ensemble Learning and Random Forests
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You, This Course and Us Source Code and PDFs Downloads Install Anaconda -
What is Machine Learning? Types of Machine Learning - Supervised Learning and Linear Regression Types of Machine Learning - Logistic Regression and Unsupervised Learning -
What is an SVM? How do they work? SVM Lab (1): Loading and examining our data set SVM Lab (2): Building and tweaking our SVM classification model -
What is a Decision Tree? Building a Decision Tree - Decision Tree Learning Building a Decision Tree - Information Gain and Gini Impurity Decision Trees Lab (1): Building our first Decision Tree Decision Trees Lab (2): Viewing and tweaking our Decision Tree -
What is Overfitting? And Why is it a Problem? Avoiding Overfitted Models - Cross Validation and Regularization -
Teamwork: How Ensembles like Random Forest Mitigate the Problem of Overfitting Random Forest Lab: Use an Ensemble of Decision Trees to Get Better Results
Course Description
What will I learn?
- Have a broad understanding of ML and hands on experience with building classification models using Support Vector Machines, Decision Trees and Random Forests in Python's scikit-learn
About the course
This course will give you a fundamental understanding of Machine Learning overall with a focus on building classification models. Basic ML concepts of ML are explained, including Supervised and Unsupervised Learning; Regression and Classification; and Overfitting. There are 3 lab sections which focus on building classification models using Support Vector Machines, Decision Trees and Random Forests using real data sets. The implementation will be performed using the scikit-learn library for Python.
The Intro to ML Classification Models course is meant for developers or data scientists (or anybody else) who knows basic Python programming and wishes to learn about Machine Learning, with a focus on solving the problem of classification.
Who should take the course?
- Developers and data scientists who wish to learn how to build classification models in ML
Pre-requisites & Requirements
- Basic Python programming