Machine Learning with Python

Lecture 2.1

Basic Syntax.

Lecture 2.2

Data Types.

Lecture 2.3

Operators.

Lecture 2.4

Decisions.

Lecture 2.5

Loops.

Lecture 2.6

Strings.

Lecture 2.7

Functions.

Lecture 2.8

Most needed library and tools.

Lecture 3.1

Classes.

Lecture 3.2

Objects.

Lecture 3.3

Data members.

Lecture 3.4

Overloading.

Lecture 3.5

Inheritance.

Lecture 4.1

List and tuples.

Lecture 4.2

Sets.

Lecture 4.3

Dictionaries.

Lecture 5.1

Reading from Files.

Lecture 5.2

Writing into files.

Lecture 5.3

Database Connections.

Lecture 5.4

Dealing with files using pandas.

Lecture 6.1

Multi threads.

Lecture 6.2

Sockets.

Lecture 6.3

APIs.

Lecture 7.1

Introduction to data science

Lecture 7.2

Data science trends?

Lecture 7.3

Statistics Refreshment (Descriptive and Inferential)

Lecture 7.4

Mathematics Refreshment.

Lecture 8.1

Linear Regression.

Lecture 8.2

Logistic Regression.

Lecture 9.1

Clustering.

Lecture 10.1

What‘s Machine Learning?

Lecture 10.2

What’s Deep Learning?

Lecture 10.3

Neural Networks

Lecture 10.4

Neural Network Architecture and How it works

Lecture 10.5

Neural Network Implementing

Lecture 10.6

Intro to Tensor Flow

Lecture 10.7

Data Visualization

Lecture 10.8

Recommended Systems

Lecture 11.1

FULL BUSINESS CASE