Tutors

Innomatics Research Labs

PROGRAM OVERVIEW

CURRICULUM

  Application Development - Flask

  • Flask Class - 1
  • HTML File reading with Flask
  • Flask Jijnja Templating
  • Jinja Templating Flask - II
  • Data Base Connection using Flask
  • Flask Class Files

  Application Development - Streamlit

  • Problem Solving with Python Programming
  • Introduction to Streamlit
  • Streamlit - II

  Intro to Machine Learning & Data Preparation

  • Intro to Machine Learning
  • Regression vs Classifications
  • Solving Your First ML Problem
  • Need of EDA in ML
  • Framwork to solve ML Problems
  • Data Pre Processing - Numerical and Categorical Columns
  • Case Study - Diamond Price Prediction with Data Preparation
  • Introduction to Text Pre Processing
  • Case Study - Spam Detection
  • Word cloud - Visualizing Text Data
  • Case Study Hand digit written classification and Working with Image Data

  Machine Learning - Supervised Learning

  • Project Discussion and Mathematical Foundations
  • Distance and Dot Product Between Two Vectors
  • Line of Equation
  • kNN for Classification Task
  • kNN for Regression Task
  • Introduction to Linear Regression
  • Simple and Multiple Linear Regression
  • Logistic Regression - 1
  • Logistic Regression - II
  • Logistic Regression - III
  • Intro to Gradient Descent
  • Gradient Descent with Linear Regression
  • Gradient Descent with Logistic Regression + Lab
  • Model Productization - Serialization and Deserialization Lab
  • Model Productization - Application Integration
  • Intro to Probability
  • Naive Bayes Algorithm
  • Solving An Example Using Naive Bayes
  • Revision - ML Algorithms
  • Introduction to Decision Tree
  • Solving a Classification Problem with Decision Tree
  • Decision Tree Regression and Intro to Random Forest
  • RF Classification and Regression + Evaluation Metrics
  • Performance Metrics - II
  • Performance Metrics with Lab Classification
  • Logistic Regression (Maximum Likelihood Estimation MLE)
  • Model Selection - Part 1
  • Model Selection - Part 2
  • Model Selection - Part 3
  • Model Selection - Part 4
  • Model Selection - Part 5
  • Feature Engineering - 1
  • Feature Engineering - 2
  • Feature Engineering - Part 3
  • Ensemble - Part 1 with Lab
  • Ensemble Models - Part 2 with Lab
  • DT for Regression and SVM
  • SVM with Kernel Trick
  • SVM and Logistic Regression
  • Assumption of Linear Regression and Treating Multicollinearity
  • Unsupervised Learning - K Means Clustering Algorithm
  • K Means++ and Hierarchical Clustering with Lab
  • Customer Segmentation and PCA
  • Principal Component Analysis with Lab

  Assignment - Python Programming

  • Task - 1 (Basic Python Programming)
  • Task - 2 (Data Types)
  • Task - 3 (Python Maths)
  • Task - 4 (Basic Python - Strings)
  • Task - 5 (RegEx)

  Assignment - Machine Learning

  • Project 1 - Churn Analysis (Data Preparation and Model Building)
  • KNN Regression from Scratch
  • Handwritten and Sentiment Project - SPRINT - 2

  Project - Machine Learning

  • Team Submission 1
  • Team Submission 2
  • Team Submission 3
  • ML - Regression Concrete Strength Prediction

  Tableau Module

  • Day 1 Tableau File
  • Super Store Dataset
  • Tableau - 1 Introduction
  • Tableau Installation Procedure Files
  • Tableau - 2 Data Types Column Formatting Sorting Hierarchy Types of Files Discrete vs Continuous
  • Tableau - 3 Groupby and Filters
  • Tableau 4 - Parameter (Filter & Set) & Data Relationship
  • Tableau 5 - Rank Shapes Models Forecasting Clustering Summarization and Parameters as measures
  • Tableau Tasks (18 Excercises)
  • Tableau 6 - Functions - Number & string Actions at Worksheet Maps, Animation Pages
  • Tableau 7 - Dash board Story Board Publishing Extract Data and Charts

  Placement and Interview Preparation Sections

  • SQL Interview Preparation Session
  • Python Interview Preparation Session
  • Mastering LinkedIn Session for Effective Job Search
  • Resume Building Session

  Misc. Lectures

  • File Handling
  • File Handling - II
  • Functions
  • Functions and Modules
  • Modules (05-10-2021)
  • Class and Object (08-10-2021)

  SQL Module - Urvinder Singh (On-going)

  • MySQL Installation Step by Step Procedure
  • MySql Installation Processor File
  • SQL 1 - Intro to SQL, Roles and Importance & RDMS and its Models
  • RDBMS - Advantages & disadvantages types of normalization Basics of SQL structs(DDL DML ETC)
  • Intro to SQL , Structs in SQL Constraints in SQL, Data Types in SQL Hands-on session on DDL
  • DML DCL TCL Sql operators Sql functions
  • HR Schema Database File
  • SQL Basic and HR Employee Lab - Part 1 File
  • SQL Part-1- Database Management System and Normalization PDF File
  • SQL Part-2 Basic SQL_Subset_Operators_Functions_SetOperators_Joins PDF
  • Complete Lab on all the Basic and Group by Order by Using HR Employee - Part 1
  • Lab - join Rank Rank dense concept Sub query
  • Lab File - join Rank Rank dense concept Sub query

  Technical Assessment - MCQ's

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  • Python Technical Assessment
  • Python Coding Questions