Python Overview
About Interpreted Language
Advantages/ Disadvantages of Python pydoc.
Starting Python
Interpreter Path
Using the Interpreter
Running a Python script
Using Varriables
Keywords
Built-in Functions
Strings Different Literals
Math Operators and Expressions
Writing to the Screen
String Formatting
Command Line Parameters and Flow Controls

Lists
Tuples
Indexing & Slicing
Iterating throw a Sequences
Using Enumerate()
Operators and Keywords for Sequences
The xrange() Function
List Comprehensions
Generator Expressions
Dictionaries and Sets

Functions
Function Parameters
Global Varriables
Alternate Keys
Lambda expressions
Sorting Collections of Collection
Sorting Dictionaries
Sorting List in Place
Errors and Exception Handling
Handling multiple Exceptions
The Standard Exception hierarchy
Using Modules
The Import statements
Module Search Path
Package Installation Ways

The Sys Module
Interpreter Information
STDIO
Launching External Programs
Paths Directories and Filenames
Walking Directory Trees
Maths Function
Random Numbers
Dates and Times
Zipped Archives
Introduction to Python Classes
Defining Classes
Initializes
Instances Methods
Properties
Class Methods and DataStatic Methods
Private Methods and Inheritance
Module aliases and Regular Expression

Debugging
Dealing with Errors
Creating a Database with SQLite3
CRUD Operations
Creating a Database Object

Learning NumPy
Plotting using matplotlib and Seabron
Machine Learning Application
Introduction to Pandas
Creating Data Frames
Grouping Sorting
Plotting Data
Creating Functions
Converting Different Formats
Combining Data from Various Formats
Slicing / Dicing Operations

Various Machine Learning Algorithm in Python
Apply Machine learning Algorithm in Python

How to select the right Data
Which are the best feature to use
Additional features selection techniques
A Feature selection case study
Preprocessing Introduction
Preprocessing Scaling techniques
How to Preprocess your Data
How to Scale your Data
Feature Scaling Final Project

Highly Efficient machine Learning Algorithms
Bagging Decision Trees
The Power of ensembles
Random Forest Ensemble technique
Boosting – Adaboost
Boosting enesemble stochastic gradient boosting
A final ensemble technique

Introduction Model Tuning
Parameter Tuning GridSearchCV
A Second method to tune your Algorithm
How to automate machine learning
Which ML Algo should you choose
How to compare machine learning Algorithms in practice

Neural Networks Introduction
What is deep learning
What is one hot encoding
How to implement one hot encoding
How to handle missing values
How to impute missing Values
Introducing the MNIST dataset

Programming a neural network in tensorflow
Programming a neural network-Multilayer perceptron in tensorflow

Introduction to keras – a convient way to code neural networks
What is a convolutional neural network
How does a cnn work

Creating a convolutional neural network from scratch
What are RNNs – Introduction to RNNs
Recurrent Neural Network rnn in python
LSTMs for Begginners – Understanding LSTMs
Long short term memory neural network LSTM in Python