Beginners Guide To Machine Learning - Python, Keras, Sklearn

The foundations of machine learning, taught in an engaging and concise way

What you'll learn

Gain a foundational understanding of machine learning

Implement both supervised and unsupervised machine learning models

Measure the performances of different machine learning models using the suitable metrics

Understand which machine learning model to use in which situation

Reduce data of higher dimensions to data of lower dimensions using principal component analysis

Requirements

A windows machine, and a willingness to learn

Description

In this course, we will cover the foundations of machine learning. The course is designed to not beat around the bush, and cover exactly what is needed concisely and engagingly. The content found in this course is essentially the same content that can be found in a University level machine learning module.  Through the use of entertaining stories, professionally edited videos, and clever scriptwriting, this course allows one effectively absorb the complex material, without experiencing the usual boredom that can usually be experienced when trying to study machine learning content.   The course first goes into a very general explanation of machine learning. It does this by telling a story that involves an angry farmer and his missing donuts. This video sets the foundation for what is to come. After a general understanding is obtained, the course moves into supervised classification. It is here that we are introduced to neural networks through the use of a plumbing system on a flower farm.Thereafter, we delve into supervised regression, which is explained with the help of a quest to find the most optimally priced real estate in town. We then cover unsupervised classification and regression by using other farm-based examples.This course is probably the best foundational machine learning course out there, and you should definitely give it a try!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 What exactly is machine learning?

Section 2: Installing tensorflow, python, jupyter notebook, numpy, pandas, sklearn

Lecture 3 Installing Python and Jupyter Notebook

Lecture 4 Installing tensorflow, numpy, pandas, and sklearn

Section 3: Supervised Machine Learning

Lecture 5 Introduction to Neural Networks

Lecture 6 Maths behind Neural Networks

Lecture 7 Supervised Classification model implementation - Flower prediction(Iris dataset)

Lecture 8 Supervised Regression explained

Lecture 9 Supervised Regression Implementation - House price predictor

Lecture 10 Bias and variance

Lecture 11 Decision Trees

Lecture 12 No Free Lunch Theorem

Section 4: Unsupervised Classification

Lecture 13 K-Means Clustering explained

Lecture 14 K-Means Clustering implementation

Section 5: Unsupervised Regression

Lecture 15 Dimensionality reduction explained - Principal component analysis

Lecture 16 PCA Implementation

Section 6: Ensemble learning

Lecture 17 Ensemble learning explained

Lecture 18 Ensemble model implementation

Section 7: Measuring the performance of machine learning algorithms

Lecture 19 Comparing classification algorithms

Lecture 20 Ending note

Beginners to machine learning. College students looking to improve their capability. Professionals looking to implement machine learning in their day to day business.