COURSE DESCRIPTION
The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. In this series, we’ll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we’ll apply the algorithms in code using real world data sets along with a module, such as with ScikitLearn. Finally, we’ll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved. This should give you a complete understanding of exactly how the algorithms work, how they can be tweaked, what advantages are, and what their disadvantages are. In order to follow along with the series, I suggest you have at the very least a basic understanding of Python. If you do not, I suggest you at least follow the Python Basics tutorial. Most of the machine learning algorithms are actually quite simple, since they need to be in order to scale to large datasets. Math involved is typically linear algebra, but I will do my best to still explain all of the math. If you are confused/lost/curious about anything, ask in the community here. You will also need ScikitLearn and Pandas installed, along with others that we’ll grab along the way. Machine learning was defined in 1959 by Arthur Samuel as the “field of study that gives computers the ability to learn without being explicitly programmed.” This means imbuing knowledge to machines without hardcoding it.
CERTIFICATION
On completion.
LEARNING OUTCOME
 Understanding of Machine Learning
 Practical and Theoretical Application
 Inner Workings of Supervised and Unsupervised
 Understanding Deep Algorithms
 Knowing linear Regression and K Nearest Neighbors
 Support Vector Machines (SVM)
 Flat Clustering and Hierarchical Clustering
 Neural Networks
 And Many More …
Course Features
 Lectures 24
 Quizzes 0
 Duration
 Skill level All levels
 Language English
 Students 9
 Assessments Yes

Practical Machine Learning Tutorial with Python Intro
 Practical Machine Learning Tutorial with Python Intro p.1
 Regression Intro – Practical Machine Learning Tutorial with Python p.2
 Regression Features and Labels – Practical Machine Learning Tutorial with Python p.3
 Regression Training and Testing – Practical Machine Learning Tutorial with Python p.4
 Regression forecasting and predicting – Practical Machine Learning Tutorial with Python p.5
 Pickling and Scaling – Practical Machine Learning Tutorial with Python p.6
 Regression How it Works – Practical Machine Learning Tutorial with Python p.7
 How to program the Best Fit Slope – Practical Machine Learning Tutorial with Python p.8
 How to program the Best Fit Line – Practical Machine Learning Tutorial with Python p.9
 R Squared Theory – Practical Machine Learning Tutorial with Python p.10
 Programming R Squared – Practical Machine Learning Tutorial with Python p.11
 Testing Assumptions – Practical Machine Learning Tutorial with Python p.12
 Classification w/ K Nearest Neighbors Intro – Practical Machine Learning Tutorial with Python p.13
 K Nearest Neighbors Application – Practical Machine Learning Tutorial with Python p.14
 Euclidean Distance – Practical Machine Learning Tutorial with Python p.15
 Creating Our K Nearest Neighbors Algorithm – Practical Machine Learning with Python p.16
 Writing our own K Nearest Neighbors in Code – Practical Machine Learning Tutorial with Python p.17
 Applying our K Nearest Neighbors Algorithm – Practical Machine Learning Tutorial with Python p.18
 Final thoughts on K Nearest Neighbors – Practical Machine Learning Tutorial with Python p.19
 Support Vector Machine Intro and Application – Practical Machine Learning Tutorial with Python p.20
 Understanding Vectors – Practical Machine Learning Tutorial with Python p.21
 Support Vector Assertion – Practical Machine Learning Tutorial with Python p.22
 Support Vector Machine Fundamentals – Practical Machine Learning Tutorial with Python p.23
 Support Vector Machine Optimization – Practical Machine Learning Tutorial with Python p.24