• Understanding Data Types in Python
• Control flow statements: 
• If, Elif and Else
• Definite and Indefinite loops: 
• For and While loops
  
  

Functions

• Built In Functions in Python
• Paramerized functions
• Writing user-defined functions in Python

Oops

• Class
• Object
• Constructor

Arrays

• List
• List comprehensions and Lambda
• Parsing information with Python
• Dictionaries
• Tuples

Python Programming Language

• Statistical Hypothesis 
• Testing Python Hypothesis
• Testing Matplotlib Numpy

Pandas Scipy Python 

Lambdas Python 

Regular Expressions

Introduction to NumPy

• The Basics of NumPy Arrays
• Computation on NumPy Arrays: 
• Universal Functions Aggregations: 
• Min, Max 
• Everything in Between Computation on Arrays: 
• Broadcasting Comparisons, 
• Masks, Boolean Logic Fancy 
• Indexing Sorting 

Arrays Structured Data: 

NumPy’s Structured Arrays

Advanced NumPy

Introducing Pandas Objects

• Data Indexing and Selection
• Operating on Data in Pandas, 
• Handling Missing Data
• Hierarchical Indexing 

Combining Datasets: 

• Concat and Append Combining Datasets: 
• Merge and Join
• Aggregation and Grouping and Pivot Tables
• Vectorized String 

Operations Working with Time Series

High-Performance Pandas: 

• eval() and query()
• Visualization with Matplotlib
• Simple Line Plots
• Simple Scatter Plots
• Visualizing Errors, 
• Density and Contour Plots
• Histograms, 
• Binnings, and Density
• Customizing Plot Legends

• Data Loading, Storage, and File Formats 
• Data Cleaning and Preparation Data Wrangling Plotting and Visualization 
• Data Aggregation and 
• Group Operations 
• Time Series 

Advanced pandas 

• Introduction to Modeling Libraries in Python Data Analysis Examples 
• Customizing Colorbars 
• Multiple Subplots Text 
• and Annotation Customizing Ticks 

Customizing Matplotlib: 

• Configurations and Stylesheets 
• Three-Dimensional Plotting in Matplotlib 
• Geographic Data with Basemap Visualization with Seaborn 

What Is Machine Learning? 

• Introducing Scikit
• Learn Hyper parameters and Model Validation 
• Feature Engineering 
• Naive Bayes Classification 
• Linear Regression Support 
• Vector Machines 
• Decision Trees and Random Forests Principal Component Analysis 
• Manifold Learning k-Means Clustering 
• Gaussian Mixture Models 
• Kernel Density Estimation 
• A Face Detection Pipeline 

Tensorflow

• Variables and Placeholders 
• TensorFlow – A Neural Network 
• TensorFlow Regression 
• TensorFlow Classification 
• TF Classification 
• Saving and Restoring Models 
• Convolutional Neural Networks

Introduction to Convolutional Neural Network Section 

• Review of Neural Networks 
• MNIST
• MNIST Data Overview

Kera

• Deep Nets with Tensorflow  
• Abstractions API – Keras 

Sql

Tableau