Master the fundamentals of Machine Learning in 2 hours!
New
Created by SA Programmer
English
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. 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.
Who this course is 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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Course content
10 sections • 20 lectures • 1h 51m total lengthCollapse all sections
Introduction2 lectures • 19min
- Introduction02:22
- What exactly is machine learning?16:21
Installing tensorflow, python, jupyter notebook, numpy, pandas, sklearn2 lectures • 4min
- Installing Python and Jupyter Notebook03:37
- Installing tensorflow, numpy, pandas, and sklearn00:51
Supervised Classification3 lectures • 27min
- Introduction to Neural Networks10:49
- Maths behind Neural Networks06:11
- Supervised Classification model implementation – Flower prediction(Iris dataset)10:23
Supervised Regression2 lectures • 12min
- Supervised Regression explained03:07
- Supervised Regression Implementation – House price predictor09:13
No Free Lunch Theorem3 lectures • 13min
- Bias and variance07:19
- Decision Trees03:57
- No Free Lunch Theorem02:08
Unsupervised Classification2 lectures • 10min
- K-Means Clustering explained03:46
- K-Means Clustering implementation05:50
Unsupervised Regression2 lectures • 8min
- Dimensionality reduction explained – Principal component analysis04:35
- PCA Implementation03:19
Ensemble learning2 lectures • 7min
- Ensemble learning explained01:47
- Ensemble model implementation04:58
Measuring the performance of machine learning algorithms1 lecture • 11min
- Comparing classification algorithms10:48
Final word1 lecture • 1min
- Ending note00:22
