DATA SCIENCE & ML USING Python Training

This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science! Python is a general-purpose interpreted, interactive, object-oriented and high-level programming language. Currently Python is the most popular Language in IT. Python adopted as a language of choice for almost all the domain in IT including Web Development, Cloud Computing (AWS, OpenStack, VMware, Google Cloud, etc.. ), Infrastructure Automations , Software Testing, Mobile Testing, Big Data and Hadoop, Data Science, etc. This course to set you on a journey in python by playing with data, creating your own application, and also testing the same.

An Introduction to Python

  • Brief History
  • Why Python
  • Where to use

Beginning Python Basics

  • The print statement
  • Comments
  • Python Data Structures & Data Types
  • String Operations in Python
  • Simple Input & Output
  • Simple Output Formatting

Python Program Flow

  • Indentation
  • The If statement and its' related statement
  • An example with if and it's related statement
  • The while loop
  • The for loop
  • The range statement
  • Break & Continue
  • Assert
  • Examples for looping

Functions & Modules

  • Create your own functions
  • Functions Parameters
  • Variable Arguments
  • Scope of a Function
  • Function Documentation/Docstrings
  • Lambda Functions & map
  • An Exercise with functions
  • Create a Module
  • Standard Modules

Exceptions

  • Errors
  • Exception Handling with try
  • Handling Multiple Exceptions
  • Writing your own Exceptions

File Handling

  • File Handling Modes
  • Reading Files
  • Writing & Appending to Files
  • Handling File Exceptions
  • The with statement

Classes In Python

  • New Style Classes
  • Variable Type
  • Static Variable in class
  • Creating Classes
  • Instance Methods
  • Inheritance
  • Polymorphism
  • Encapsulation
  • Scope and Visibility of Variables
  • Exception Classes & Custom Exceptions

Regular Expressions

  • Simple Character Matches
  • Special Characters
  • Character Classes
  • Quantifiers
  • The Dot Character
  • Greedy Matches
  • Grouping
  • Matching at Beginning or End
  • Match Objects
  • Substituting
  • Splitting a String
  • Compiling Regular Expressions
  • Flags

Data Structures

  • List Comprehensions
  • Nested List Comprehensions
  • Dictionary Comprehensions
  • Functions
  • Default Parameters
  • Variable Arguments
  • Specialized Sorts
  • Iterators
  • Generators
  • The Functions any and all
  • The with Statement
  • Data Compression
  • Closer
  • Decorator

Writing GUIs in Python

  • Introduction
  • Components and Events
  • An Example GUI
  • The root Component
  • Adding a Button
  • Entry Widgets
  • Text Widgets
  • Checkbuttons
  • Radiobuttons
  • Listboxes
  • Frames
  • Menus
  • Binding Events to Widgets

Thread In Python

  • Thread life Cycle
  • Thread Definition
  • Thread Implementation

Network Programming

  • Introduction
  • A Daytime Server
  • Clients and Servers
  • The Client Program
  • The Server Program
  • Client and Server Architecture
  • Threaded Server

Python & MySQL Database Connection

  • Introduction
  • Installation
  • DB Connection
  • Creating DB Table
  • Insert, Read,Update, Delete operations
  • Commit & Rollback operation
  • Handling Errors
  • Mini project on Python with TKinter

Data Science & Machine Learning

Course Overview

  • Overview of Data science
  • What is Data Science
  • Different Sectors Using Data Science

Mathematical Computing with python(NumPy)

  • Introduction to Numpy
  • Activity-Sequence
  • Creating and Printing an ndarray
  • Class and Attributes of ndarray
  • Basic Operations
  • Activity – Slicing
  • Copy and Views
  • Mathematical Functions of Numpy
  • Advance Slicing
  • Transpose and arance
  • Searching

Data Manipulation with Pandas

  • Introduction of Pandas
  • Data Types in Pandas
  • Understanding Series
  • Understanding DataFrame
  • View and Select Data Demo
  • Missing Values
  • Data Operations
  • File Read and Write Support
  • Pandas Sql Operation

Python for Data Visualization-Matplotlib

  • Introduction to Matplotlib
  • Matplotlib Part 1 Set up
  • Matplotlib Part 2 Plot
  • Matplotlib Part 3 Next steps
  • Matplotlib Exercises Overview
  • Matplotlib Exercises – Solutions

Introduction to Machine Learning

  • Introduction to Machine Learning
  • Machine Learning with Python

Linear Regression

  • Linear Regression Theory
  • Model selection Updates for SciKit Learn
  • Linear Regression with Python
  • Linear Regression Project Overview and Project Solution

Logistic Regression

  • Logistic Regression Theory – Introduction
  • Logistic Regression with Python – Part 1 – Logistics
  • Logistic Regression with Python – Part 2 – Regression
  • Logistic Regression with Python – Part 3 – Conclusion
  • Logistic Regression Project Overview and Project Solutions

K Nearest Neighbours

  • KNN Theory
  • KNN with Python
  • KNN Project Overview and Project Solutions

Decision Trees and Random Forests

  • Introduction to Tree Methods
  • Decision Trees and Random Forest with Python
  • Decision Trees and Random Forest Project Overview
  • Decision Trees and Random Forest Solutions Part 1
  • Decision Trees and Random Forest Solutions Part 2

Support Vector Machines

  • SVM Theory
  • Support Vector Machines with Python
  • SVM Project Overview
  • SVM Project Solutions

K Means Clustering

  • K Means Algorithm Theory
  • K Means with Python
  • K Means Project Overview
  • K Means Project Solutions

Machine Learning Basics

  • Converting business problems to data problems
  • Understanding supervised and unsupervised learning with examples
  • Understanding biases associated with any machine learning algorithm
  • Ways of reducing bias and increasing generalisation capabilites
  • Drivers of machine learning algorithms
  • Cost functions
  • Brief introduction to gradient descent
  • Importance of model validation
  • Methods of model validation
  • Cross validation & average error

Generalised Linear Models in Python

  • Linear Regression
  • Regularisation of Generalised Linear Models
  • Ridge and Lasso Regression
  • Logistic Regression
  • Methods of threshold determination and performance measures for classification score models

Tree Models using Python

  • Introduction to decision trees
  • Tuning tree size with cross validation
  • Introduction to bagging algorithm
  • Random Forests
  • Grid search and randomized grid search
  • ExtraTrees (Extremely Randomised Trees)
  • Partial dependence plots

Boosting Algorithms using Python

  • Concept of weak learners
  • Introduction to boosting algorithms
  • Adaptive Boosting
  • Extreme Gradient Boosting (XGBoost)

Support Vector Machines (SVM) & kNN in Python

  • Introduction to idea of observation based learning
  • Distances and similarities
  • k Nearest Neighbours (kNN) for classification
  • Brief mathematical background on SVM/li>
  • Regression with kNN & SVM

Unsupervised learning in Python

  • Need for dimensionality reduction
  • Principal Component Analysis (PCA)
  • Difference between PCAs and Latent Factors
  • Factor Analysis
  • Hierarchical, K-means & DBSCAN Clustering

Text Mining in Python

  • Gathering text data using web scraping with urllib
  • Processing raw web data with BeautifulSoup
  • Interacting with Google search using urllib with custom user agent
  • Collecting twitter data with Twitter API
  • Naive Bayes Algorithm
  • Feature Engineering with text data
  • Sentiment analysis

OpenCV

  • Basic of Computer Vision & OpenCV
  • Images Manipulations
  • Image Segmentation
  • Object Detection
  • Face, People and Car Detection
  • Face Analysis and Fulters
  • Machine Learning in Computer Vision
  • Motion Analysis & Object Tracking
  • Project on OpenCV
COMMENCING NEW BATCHES
ENQUIRY FORM
FOLLOW US ON
SUBSCRIBE TO OUR NEWSLETTER

WE ACCEPT ONLINE PAYMENTS
PAY ONLINE