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
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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!
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