Learn Machine Learning from scratch with Python which as combination paves way for many huge job opportunities. This is a complete beginner course, you can start with this even if you don’t have any prior coding experience. Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.

Course content

Core Python

Getting Started

• History
• A Python Q&A Session
• How Python Runs Programs
• How You Run Programs

Introduction to Python:

• What is Python?
• Why Python?
• Python Applications in real life
• Brief history of Python
• Versions of Python
• Installing Python
• Using IDLE
• First Python Program
• Getting help from Python Docs

Basic Syntax

• Basic syntax
• Commenting
• Indentation
• Python keywords
• Strings
• String values
• String Operations
• String slicing
• Built in string methods
• Formatted printing
• Simple Input and Output handling

Variables

• Variables
• Data type
• Number
• string
• List
• Tuple
• Dictionary

Types and Operations

• Introducing Python Object Types
• Numeric Types
• The Dynamic Typing Interlude
• Strings
• Lists and Dictionaries
• Tuples, Files and Everything Else

Variables Data types

• Intro to dynamic typing
• Variables in Python
• Naming conventions
• Basic Data types (representation of strings, integer, floats)

Decision Making – Loops 

• While loop, if loop and nested loop
• Number type conversion – int (), long (). Float ()
• Mathematical functions, Random function, Trigonometric function

Language Building blocks

• Control statements, the if, elif, else
• True and False
• Arithmetic Operators
• Relational Operators
• Logical Operators
• Bitwise Operators
• While loop
• Usage of pass, break and continue
• For each loop

String

• Strings- Escape char, String special Operator, String formatting Operator
• Build in string methods – center (), count () decode (), encode ()

List

• Python List – Accessing values in list, delete list elements, Indexing slicing & Matrices
• Built in Function – cmp(), len(), min(), max()

Dictionary

• Dictionary – Accessing values from dictionary, Deleting and updating elements in Dict.
• Properties of Dist., Built in Dist functions & Methods.
• Date & time -Time Tuple, calendar module and time module

Collections

• Lists
• Tuples
• Sets
• Dictionaries
• Sorting collections
• Operations on collections
• Discussion on real life application of above collections

Functions

• Introduction to functions
• Built in functions
• User defined functions
• Function parameters
• Variable arguments, args and kwargs
• Positional and named arguments
• Discussion scope of variables with respect to functions and namespace
• Passing function to another function

Files

• Files in Python- Reading keyboard input, input function
• Opening and closing files. Syntax and list of modes
• Files object attribute- open, close . Reading and writing files, file Position.
• Renaming and deleting files

Directories

• mkdir methid, chdir () method , getcwd method , rm dir

File Handling

Exception Handling

• Exception handling – List of exceptions – Try and exception
• Try- finally clause and user defined exceptions

Modules

• Introduction to modules
• Introduction to standard modules
• OS module
• path module
• Sys module
• sub process module
• Argument parsing using argparse module
• .csv file parsing using csv module
• .jason file paring using Jason module
• Xml file parsing using xml module
• Introduction to logging module

Object Oriented Programming

• Introduction to Classes and Objects
• Principles of OOP
• Instance methods
• Special methods
• Encapsulation
• Inheritance
• Polymorphism

Regular Expressions

• Introduction to regular exceptions
• Introduction to re module
• Simple character matches
• Match function
• Searching function
• Regular expression patterns
• Patterns in Regex
• Search And Replace

GUI Programming

• Introduction
• Tkinter programming
• Tkinter widgets

My SQL

• Data base connectivity
• Methods- MySQL, Oracle, how to install MYSQL, DB connection
• Create, insert, update and delete operation, Handling erros

Multi-Threading

• Into Multi-Threading
• Threading module
• creating thread
• Synchronizing threads
• Multithreaded Priority Queue

Optional I (For testers)

• Introduction to testing using Python
• Introduction to test automation
• Introduction to Selenium web deriver
• Web testing using selenium

Option II (   Developers)

• Generators
• Decorators
• Iterators and iterator protocol
• Debugging using PDB

Options III (Web programming)

• Introduction to web programming using Python
• Introduction to Django/Flask
• Introduction to Restful API’s using Python

Option IV (Data Science)

• Introduction to data science using python
• Introduction to panda’s module
• Introduction to data visualization using matplotlib
• Introduction to NumPy
• Introduction to SciPy

Advanced Python

Functional Programming

• Lambdas
• List Comprehensions
• Set and Dictionary Comprehensions
• Closures and Decorators
• Generators and Coroutines
• Generator Expressions
• Declarative Programming

Systems Programming

• File Descriptors
• Reading and Writing Files
• Files and Directories
• File Locking
• Memory Mapped I/O
• Creating Processes
• Process Management
• Pipes and Signals

Classes and Objects

• New Style Classes
• Inheritance and Mixins
• Properties and Slots
• Static and Class Methods
• Abstract Base Classes
• Method Overriding
• Attributes and Functors
• Decorators and Factories
• Descriptors and MetaClasses

Persistence and Databases

• Shelve and Pickle
• SQL Relational Databases
• Connection, Cursor, Row Objects
• Create, Read, Update, Delete
• Error Handing
• Query Results and Metadata
• Create and Aggregate Functions
• Exporting and Importing
• Transactions and Rollbacks
• Database Objects

Network Programming

• Sockets and Addresses
• Establishing Connections
• TCP Clients and Servers
• UDP Clients and Servers
• UDS Clients and Servers
• Network Objects
• SocketServers
• Secure Sockets Layer

Web Programming

• JSON and XML
• Using XML-RPC
• Rest Interfaces
• WSGI and HTML
• Flask Framework
• Controller Functions
• Templates and Forms
• Database ORMs

 Threads and Concurrency

• Creating and Joining Threads
• Daemon Threads
• Thread Objects
• Timer Threads
• Locks and Semaphores
• Events and Conditions
• Thread Locals
• Thread Queues
• Process Queues and Tasks
• Process Pools

Inheritance

• The DRY Principle Revisited
• Single Inheritance
• Sub-Classing Classes from Python Packages
• Overriding Methods
• Calling the Parent Method with super()
• Multiple Inheritance
• Method Resolution Order

Extending and Embedding Python

• Calling C/C++ from Python
• Using ctypes
• Extension Modules in C/C++
• Raising Python Exceptions
• Calling Python from C/C++
• Embedding Python Interpreter
• Importing Python Modules from C/C++
• Converting Python Objects to C/C++
• Invoking Python Functions from C/C++

Data analysis using Numpy

• Introduction to Numpy arrays
• Creating and applying functions
• Numpy Indexing and selection Numpy Operations
• Exercise and assignment challenge

Pandas and advanced analysis

• Panda’s series
• Introduction to Data Frames
• Missing data
• Group by
• Merging, joining and concatenating
• Operations
• Data Input and Output

Data visualization with Python

• Plotting using Mat plot lib
• Sea born visualization
• Pandas built-in data visualization

Seaborn visualization

• Categorial Plot using Seaborn
• Distributional plots using Seaborn
• Matrix plots
• Grids
• Seaborn exercises

Django

 Introduction to Django

• Introduction
• Why Django?
• Batteries Included
• Django Principles
• What you Should Already Know
• Course Overview

Installing Django

• Intro
• Choosing your Versions
• Installing Pip and Python on Windows
• Demo: Windows Installation
• Installing Pip and Python on Mac OS X
• Demo: OS X Installation
• Installing Pip and Python on Linux
• Demo: Linux Installation
• Virtualenv
• Demo: Virtualenv
• Installing Django
• Summary

Starting a Django Project

• Introduction
• Creating a Django Project
• Demo: Creating a Django Project
• The Model-Template-View Pattern
• Demo: Hello, World!
• Mapping URLs
• Demo: URL Mapping
• Django Views
• Demo: Templates
• Summary

Models

• Introduction
• Demo: Adding Models
• Django Model Classes
• py Database Commands
• Demo: The Admin Interface
• The Django Admin Interface
• Demo: The Model API
• Save and Delete
• The Model API
• Database Relations
• Summary

Adding a User Home Page

• Introduction
• Demo: Adding Login and Logout Views
• More about URL Mappings
• Demo: A Template for the Home Page
• Authorization with Django
• More about Django Templates
• Demo: Adding the Home View
• URL Mappings for Apps
• Demo: Template Inheritance
• Template Inheritance
• Demo: Login Required
• Demo: Showing Game Data on the Home Page
• Demo: A Custom Manager Class
• The Template Context
• Templates: For and Include Tags
• Summary

Forms

• Introduction
• Demo: Adding a HTML Form
• Using Django Forms
• Demo: Adding Stypng to the Form with Crispy-Forms
• Demo: Field Options
• Field Options
• Demo: Showing Invitations in a List
• Demo: Accepting Invitations
• Demo: Named Groups
• Named Groups in URLs
• Summary

Odds and Ends

• Introduction
• Class-based Views
• Demo: Class-based Views
• Demo: Adding User Signup
• Generic Views
• Debugging Django
• Demo: The Django Debug Toolbar
• Resources
• Summary

Machine Learning

Python for Machine Learning

• Programming Basics
• Python Data Types
• Structures and conditional statements
• Python core packages
• Introduction to Jupyter Notebook
• Introduction to Numpy and Pandas
• Data filtering and selecting
• Find duplicates and treating missing values
• Concatenate and transform data

Setting up and installations

• Installing python
• Setting up Python environment for development
• Installation of Jupyter Notebook
• How to access our course material
• Write your first program in python

 Python object and data structures operations

• Introduction to Python objects
• Number objects and operations
• Variable assignment and keywords
• String objects and operations
• Print formatting with strings

 Python statements

• Introduction to Python statements
• If, else-if and else statements
• Comparison operators
• Chained comparison operators
• What are loops?
• For loops
• While loops

File and exception handling

• Process files using python
• Read/write and append file object
• File functions
• File pointer and operations
• Introduction to error handling
• Try, except and finally

Basic Statistics for Machine Learning

• Basic Statistics and Exploratory Analysis
• Descriptive summary statistics with Numpy
• Summarize continous and categorical data
• Outlier analysis

Introduction to Machine Learning

• Overview of Supervised and Unsupervised Machine Learning
• Linear Regression
• Clustering with K-means
• Naive Bayes Classification
• Introduction to Neural Networks

 Data Processing for Machine Learning

• Advanced Data Mugging
• Outlier Analysis
• Treating for missing values
• Normalization vs Standardization of data

 Machine Learning Algorithms

• Supervised Machine Learning algorithms
• K-Nearest Neighbors (KNN) concept and application
• Naive Bayes concept and application
• Logistic Regression concept and application
• Classification Trees concept and application
• Unsupervised Machine Learning algorithms
• Clustering with K-means concept and application
• Hierarchial Clustering concept and application

 Building and Training Machine Learning models

• Setting up the project with ML workflow.
• Data Preprocessing and statistical exploration
• Building , Training and evaluation of Machine Learning Model

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