Machine Learning I

Categories: Data Science

About Course

In this course, we’ll cover the fundamentals of machine learning with a significant focus on supervised machine learning. We’ll start from introducing machine learning, the Scikit Learn library for machine learning, then we’ll delve deep into supervised learning methods i.e. Regression and Classification. We’ll close off the course by going through model evaluation by exploring metrics for regression and classification as well as how to conduct hyperparameter tuning for our machine learning models.

Acknowledgements and Attribution

This course is attributed to Jake VanderPlas’ Python Data Science Handbook alongside Andrew NG’s tutorials on Machine Learning Concepts. We also recommend checking out the StatQuest Youtube channel for faster explanations on Machine Learning Algorithms whenever you are stuck. We have added these videos to the course to help make harder concepts simpler to understand. Finally, you have notes by Cyril Michino shared on Slack or on the resources section highlighting the most important theoretic aspects of every ML algorithm covered in this course.

Course Content

1.1 Introduction to Machine Learning

What is Machine Learning

05:28
Supervised Learning – Regression

06:57
Supervised Learning – Classification

07:17
Unsupervised Learning – Clustering

08:54
Unsupervised Learning – Dimensionality Reduction

03:40
Machine Learning Fundamentals Quiz

1.2 Data Preparation with Scikit Learn

1.3 Hyperparameters & Model Validation

1.4 Feature Engineering
The previous sections outline the fundamental ideas of machine learning, but all of the examples assume that you have numerical data in a tidy, [n_samples, n_features] format. In the real world, data rarely comes in such a form. With this in mind, one of the more important steps in using machine learning in practice is feature engineering: that is, taking whatever information you have about your problem and turning it into numbers that you can use to build your feature matrix. In this section, we will cover a few common examples of feature engineering tasks: features for representing categorical data, features for representing text, and features for representing images. Additionally, we will discuss derived features for increasing model complexity and imputation of missing data. Often this process is known as vectorization, as it involves converting arbitrary data into well-behaved vectors.

1.5 Data Preparation Project

2.1 Linear Regression

Introduction to Regression

10:27
Model Representation

06:44
Cost Function

09:05
Cost Function Intuition

15:47
Cost Function Visualisation

08:34
Gradient Descent Visualisation

06:01
Gradient Descent

08:04
Gradient Descent Implementation

10:00
Gradient Descent Intuition

07:02
Learning Rate

09:03
Gradient Descent for Linear Regression

06:36
Running Gradient Descent

05:49
Linear Regression on Scikit Learn

00:00
Linear Regression Quiz

2.2 Advanced Linear Regression

Multiple Features

09:51
Vectorisation

06:55
Vectorisation Intuition

06:52
Gradient Descent for Multiple Variables

07:46
Gradient in Practical Feature Scaling

06:35
Implementing Feature Scaling

07:34
Checking for Convergence

05:40
Gradient Learning Rate

06:07
Feature Engineering

03:04
Features & Polynomial Regression

05:52
Polynomial Regression on Scikit Learn

00:00
Advanced Linear Regression Quiz

2.3 Logistic Regression

2.4 Regularisation

2.5 Regression Weekly Project

3.1 Naives Bayes & KNN

3.2 Support Vector Machines

3.3 Trees & Random Forests

3.4 Ensemble Learning Algorithms

3.5 Classification Weekly Project

4.1 Applying Machine Learning Models

4.2 Regression & Classification Metrics

4.3 Machine Learning System Design

4.5 Model Tuning Weekly Project

5.0 Conclusion and Next Steps

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About Course

Acknowledgements and Attribution

Course Content

1.1 Introduction to Machine Learning

What is Machine Learning

Supervised Learning – Regression

Supervised Learning – Classification

Unsupervised Learning – Clustering

Unsupervised Learning – Dimensionality Reduction

Machine Learning Fundamentals Quiz

1.2 Data Preparation with Scikit Learn

Introduction to Scikit Learn

Data Normalization and Standardization Quiz

1.3 Hyperparameters & Model Validation

Overview of Model Validation

Cross-Validation

Selecting the Best Model

Learning Curves

Validation in Practice

Model Validation Quiz

Categorical Features

Text Features

Images and Derived Features

Imputation of missing data

Feature Pipelines

Data Preprocessing Quiz

1.5 Data Preparation Project

Data Cleaning Project

Submit Your Project

2.1 Linear Regression

Introduction to Regression

Model Representation

Cost Function

Cost Function Intuition

Cost Function Visualisation

Gradient Descent Visualisation

Gradient Descent

Gradient Descent Implementation

Gradient Descent Intuition

Learning Rate

Gradient Descent for Linear Regression

Running Gradient Descent

Linear Regression on Scikit Learn

Linear Regression Quiz

2.2 Advanced Linear Regression

Multiple Features

Vectorisation

Vectorisation Intuition

Gradient Descent for Multiple Variables

Gradient in Practical Feature Scaling

Implementing Feature Scaling

Checking for Convergence

Gradient Learning Rate

Feature Engineering

Features & Polynomial Regression

Polynomial Regression on Scikit Learn

Advanced Linear Regression Quiz

2.3 Logistic Regression

Understanding Classification

Introduction to Logistic Regression

Decision Boundary

Cost Function

Simplified Cost Function

Gradient Descent Implementation

Multiclass Classification

Logistic Regression Quiz

2.4 Regularisation

The Problem of Overfitting

Addressing Overfitting

Cost Function

Regularised Linear Regression

Regularised Logistic Regression

Regularisation on Scikit Learn

Regularization Quiz

2.5 Regression Weekly Project

Predicting Bicycle Traffic Example

House Pricing project

Submit your Project

3.1 Naives Bayes & KNN

Naives-Bayes Classification