Introduction to ML Classification Models using scikit-learn [Video]

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Introduction to ML Classification Models using scikit-learn [Video]

Loonycorn

Wednesday, April 18, 2018

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An overview of machine learning with hands-on implementation of classification models

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Video Details

ISBN 139781789345926

Course Length2 hours and 4 minutes

Video Description

This course will give you a fundamental understanding of machine learning with a focus on building classification models. The basic concepts of machine learning (ML) are explained, including supervised and unsupervised learning; regression and classification; and overfitting. There are three 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.

Style and Approach

Hands-on course to Introduction to ML Classification Models using scikit-learn

Introduction

Install Anaconda

You, This Course and Us

What is ML?

What is Machine Learning?

Types of Machine Learning - Supervised Learning and Linear Regression

Types of Machine Learning - Logistic Regression and Unsupervised Learning

Support Vector Machines (SVMs)

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

Decision Trees

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

Overfitting - the Bane of Machine Learning

What is Overfitting? And why is it a Problem?

Avoiding Overfitted Models - Cross Validation and Regularization

Ensemble Learning and Random Forests

Teamwork: How Ensembles like Random Forest Mitigate the Problem of Overfitting

Random Forest Lab: Use an Ensemble of Decision Trees to Get Better Results

What You Will 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

Authors

Loonycorn

Loonycorn is Janani Ravi and Vitthal Srinivasan. Between them, they have studied at Stanford, been admitted to IIM Ahmedabad, and have spent years working in tech, in the Bay Area, New York, Singapore and Bangalore. Janani spent 7 years at Google (New York, Singapore); Studied at Stanford and also worked at Flipkart and Microsoft. Vitthal also worked at Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too. They think they might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why they are so excited to be here. They hope you will try their offerings, and you'll like them.

Introduction

Install Anaconda

You, This Course and Us

What is ML?

What is Machine Learning?

Types of Machine Learning - Supervised Learning and Linear Regression

Types of Machine Learning - Logistic Regression and Unsupervised Learning

Support Vector Machines (SVMs)

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

Decision Trees