Beginners Guide To Machine Learning - Python, Keras, Sklearn

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.


Homepage

https://www.udemy.com/course/beginners-guide-to-machine-learning/