Machine Learning Training With Live Projects in Hyderabad

About Course:

Machine Learning is simply making a computer perform a task without explicitly programming it. In today’s world every system that does well has a machine learning algorithm at its heart. Take for example Google Search engine, Amazon Product recommendations, LinkedIn, Facebook etc

Genius IT training institute, Hyderabad introduce you world class Machine Learning training in Hyderabad  Our ML Training includes Python Programming, Machine Learning with Python.

Python Foundation Training

  • Python Core Objects and builtin functions
  • Number Object and operations
  • String Object and Operations
  • List Object and Operations
  • Tuple Object and operations
  • Dictionary Object and operations
  • Set object and operations
  • Boolean Object and None Object
  • Different data Structures, data processing

      Conditional Statements and Loops

  • What are conditional statements?
  • How to use the indentations for defining if, else, elif block
  • What are loops?
  • How to control the loops
  • How to iterate through the various object
  • Sequence and iterable objects

     UDF Functions and Object Functions

  • What are various type of functions
  • Create UDF functions
  • Parameterize UDF function, through named and unnamed parameters
  • Defining and calling Function
  • The anonymous Functions – Lambda Functions
  • String Object functions
  • List and Tuple Object functions
  • Dictionary Object functions

      File Handling with Python

  • Process text files using Python
  • Read/write and Append file object
  • File object functions
  • File pointer and seek the pointer
  • Truncate the file content and append data
  • File test operations using os.path

      Python Modules and Packages

  • Python inbuilt Modules
  • os, sys, datetime, time, random, zip modules
  • Create Python UDM – User Defined Modules
  • Create Python Packages
  • init File for package initialization

      Exceptional Handing and Object Oriented Python

  • Python Exceptions Handling
  • What is Exception?
  • Handling various exceptions using try….except…else
  • Try-finally clause
  • Argument of an Exception and create self exception class
  • Python Standard Exceptions
  • Raising an exceptions,    User-Defined Exceptions
  • Object oriented features
  • Understand real world examples on OOP
  • Implement Object oriented with Python
  • Creating Classes and Objects,   Destroying Objects
  • Accessing attributes,   Built-In Class Attributes
  • Inheritance and Polymorphism
  • Overriding Methods,   Data Hiding
  • Overloading Operators

     Debugging, Framework & Regular expression

  • Debug Python programs using pdb debugger
  • Pycharm Debugger
  • Assert statement for debugging
  • Testing with Python using UnitTest Framework
  • What are regular expressions?
  • The match and search Function
  • Compile and matching
  • Matching vs searching
  • Search and Replace feature using RE
  • Extended Regular Expressions
  • Wildcard characters and work with them

     Database interaction with Python

  • Creating a Database with SQLite 3,
  • CRUD Operations,
  • Creating a Database Object.
  • Python MySQL Database Access
  • DML and DDL Operations with Databases
  • Performing Transactions
  • Handling Database Errors
  • Disconnecting Database

     Package Installation, Windows spreadsheet parsing and webpage scrapping

  • Install package using Pycharm
  • What is pip, easy_install
  • Set up the environment to install packages?
  • Install packages for XLS interface and XLS parsing with Python
  • Create XLS reports with Python
  • Introduction to web scraping

Data Science / Machine Learning

  • SQL – Structured Query Language
  • Data Visualization – Matplotlib, Seaborn, Plotly, Cufflinks and Pandas in-built (Python Packages/Modules)
  • Data Analysis Using Python Modules– Numpy, Pandas
  • Machine Learning Algorithims
  • Supervised Learning Regression
  • Linear Regression
  • Multiple Linear Regression
  • Bias-Variance Trade-Off
  • Classification classification modeling
  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Simple Vector Machine (SVM)
  • Decision Trees o Ensemble Methods – Random Forest
  • Bagging
  • Boosting
  • AdaBoost
  • XGBoost
  • Unsupervised Learning Clustering
  • K-Means Clustering
  • Hierarchical Clustering
  • Dimensionality Reduction
  • Linear Discriminant Analysis
  • Principal Component Analysis (PCA)
  • Reinforcement Learning
  • Natural Language Processing
  • NLTK o NLP with NLTK
  • NLTK Extensions and Explorations
  • Sentiment Analyzer
  • Description of Sentiment Analyzer
  • Pre-processing: Tokenization
  • Pre-processing: Tokens to Vectors
  • Sentiment Analysis using Decision Tree
  • Sentiment Lexicons
  • Problems
  • Hackerrank (Python and Machine Learning)
  • Hackerearth (Python and Machine Learning)
  • GeeksForGeeks (Python)
  • Kaggle (Machine Learning)
  • Python Every Day Objective Test and Each Day Problem Statement as an assignment.