Udemy

Beginners Guide to Machine Learning - Python, Keras, SKLearn

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  • 2,372 Students
  • Updated 1/2023
4.4
(138 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
1 Hour(s) 51 Minute(s)
Language
English
Taught by
SA Programmer
Rating
4.4
(138 Ratings)

Course Overview

Beginners Guide to Machine Learning - Python, Keras, SKLearn

Master the fundamentals of Machine Learning in 2 hours!

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 Content

  • 10 section(s)
  • 20 lecture(s)
  • Section 1 Introduction
  • Section 2 Installing tensorflow, python, jupyter notebook, numpy, pandas, sklearn
  • Section 3 Supervised Classification
  • Section 4 Supervised Regression
  • Section 5 No Free Lunch Theorem
  • Section 6 Unsupervised Classification
  • Section 7 Unsupervised Regression
  • Section 8 Ensemble learning
  • Section 9 Measuring the performance of machine learning algorithms
  • Section 10 Final word

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


Reviews

  • K
    Kelebogile Mangole
    4.5

    The explanation is very clear on whats the difference between traditional and machine learning engineering approaches

  • N
    Nicolette Good
    5.0

    I am amazed by how this course has surpassed my initial expectations! It is filled with profound insights and presented in a remarkably clear manner. I am truly grateful for the invaluable knowledge it has provided. The inclusion of a South African narrator adds an extra special touch, and I am thoroughly enjoying this enlightening journey with you. Thank you immensely for this incredible experience 😊 P.S As a IT Tech recruiter by day, its essential for me to understand the latest tech out there.

  • A
    Abisha
    4.0

    really good thing but need more explain

  • U
    Uzaif Talpur
    4.5

    your explanation is splendid

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