Beginners Guide to Machine Learning Python, Keras, SKLearn

Beginners Guide to Machine Learning – Python, Keras, SKLearn, available at $39.99, has an average rating of 4.7, with 20 lectures, based on 131 reviews, and has 2367 subscribers.

You will learn about 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 This course is ideal for individuals who are Beginners to machine learning. College students looking to improve their capability. Professionals looking to implement machine learning in their day to day business. It is particularly useful for Beginners to machine learning. College students looking to improve their capability. Professionals looking to implement machine learning in their day to day business.

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Summary

Title: Beginners Guide to Machine Learning – Python, Keras, SKLearn

Price: $39.99

Average Rating: 4.7

Number of Lectures: 20

Number of Published Lectures: 20

Number of Curriculum Items: 20

Number of Published Curriculum Objects: 20

Original Price: R299.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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. Based on a university level machine learning syllabus, this course aims to effectively teach, what can sometimes be dry content, through the use of entertaining stories, professionally edited videos, and clever scriptwriting. This allows one effectively absorb the complex material, without experiencing the usual boredom that can 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, by exploring how we can figure out whether certain properties are value for money or not.

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 will definitely benefit greatly from it.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: What exactly is machine learning?

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

Lecture 1: Installing Python and Jupyter Notebook

Lecture 2: Installing tensorflow, numpy, pandas, and sklearn

Chapter 3: Supervised Classification

Lecture 1: Introduction to Neural Networks

Lecture 2: Maths behind Neural Networks

Lecture 3: Supervised Classification model implementation – Flower prediction(Iris dataset)

Chapter 4: Supervised Regression

Lecture 1: Supervised Regression explained

Lecture 2: Supervised Regression Implementation – House price predictor

Chapter 5: No Free Lunch Theorem

Lecture 1: Bias and variance

Lecture 2: Decision Trees

Lecture 3: No Free Lunch Theorem

Chapter 6: Unsupervised Classification

Lecture 1: K-Means Clustering explained

Lecture 2: K-Means Clustering implementation

Chapter 7: Unsupervised Regression

Lecture 1: Dimensionality reduction explained – Principal component analysis

Lecture 2: PCA Implementation

Chapter 8: Ensemble learning

Lecture 1: Ensemble learning explained

Lecture 2: Ensemble model implementation

Chapter 9: Measuring the performance of machine learning algorithms

Lecture 1: Comparing classification algorithms

Chapter 10: Final word

Lecture 1: Ending note

Instructors

SA Programmer
Programming Lecturer and Software Engineer

Rating Distribution

1 stars: 2 votes

2 stars: 3 votes

3 stars: 17 votes

4 stars: 40 votes

5 stars: 69 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!