Tutors

Technology For All

PROGRAM OVERVIEW

CURRICULUM

  Welcome to TechnologyForAll

  • Anaconda Installation Procedure
  • Introduction to the Course

  Module 1: Python Programming

  • Introduction to Python
  • Introduction Python File
  • Basics of Python
  • Intro Python Noteboook
  • Basics of Python II
  • Datatypes
  • Mentoring Session File
  • Control Statements
  • Control Statements File
  • Functions
  • Functions II
  • Modules and Classes
  • Functions Notes I
  • Functions Notes II
  • Functions, Modules and Classes - Hands on
  • File Handling
  • Modules and Classes
  • File Handling Notes
  • Exception Handling

  Module 1: Python Assignments

  • Assignment 1
  • Assignment 2
  • Assignment 3
  • Assignment 4
  • Assignment 5

  Module 2: Data Analysis

  • Numpy
  • Numpy II
  • Numpy Notes
  • Numpy II file
  • Pandas I
  • Pandas File
  • Probability and Statistics
  • Probability and Statistics PDF
  • Random Variables and Probability Distribution
  • Random Variables and Probability Distribution PDF
  • OOPs
  • More on Statistics
  • Hypothesis Testing I
  • Hypothesis Testing II
  • Mentoring Session
  • Visualization I
  • Visualization II
  • Missing Values
  • Probability and Statistics
  • Regular Expressions
  • Regular Expressions II
  • Web Scrapping
  • Web Scraping II
  • Web Scrapping III
  • Web Scraping IV

  Module 2: Data Analysis Assignments

  • Numpy Assignment
  • Pandas Assignment
  • Visualization-1 Assignment
  • Visualization-2 Assignment

  Revision Sessions

  • Python Revision Session
  • Python File

  Module 3: Machine Learning

  • Introduction to Machine Learning
  • Lecture - 1 Introduction to Machine Learning
  • Basics of Linear Algebra
  • Lecture -2: Basics of Linear Algebra
  • Linear Regression
  • K-Nearest Neighbors Algorithm
  • KNN Implementation
  • Lecture - 3: KNN
  • Linear Regression Implementation and Gradient Descent
  • Lecture - 4 Linear Regression
  • Lecture - 5: Gradient Descent
  • Linear Regression file
  • Gradient Descent Continuation
  • Gradient Descent implementation
  • Gradient Descent continuation
  • Logistic Regression
  • Logistic Regression Implementation
  • Revision Session - Machine Learning
  • Revision Session II - Machine Learning
  • Evaluation Metrics
  • Evaluation Metrics II
  • Support Vector Machines
  • Lecture 6: Logistic Regression Notes
  • Lecture 7: Evaluation Metrics Notes
  • Lecture 8: Polynomial Regression Notes
  • Decision Trees
  • Decision Tree II
  • Lecture 10: Decision Tree Notes
  • Naive Bayes
  • Lecture 11: Naive Bayes Notes
  • Model Selection
  • Lecture 12: Model Selection Notes
  • Ensemble Methods
  • Ensemble Techniques
  • Gradient Boosting and Clustering
  • Clustering
  • PCA
  • Sentiment Analysis Case Study
  • Sentiment Analysis case study cont.

  Placement Activities

  • Resume Building and Interview Preparation Session

  Module 3: Assignments

  • Linear Regression Assignment
  • Logistic Regression Assignment
  • KNN Assignment
  • Decision Tree Assignment
  • Ensemble Algorithms Assignment
  • SVM Assignment

  Internship Tasks

  • Web Scraping Project
  • Machine Learning Project (Option 1)
  • Machine Learning Project (Option 2)
  • Deep Learning Project

  Module 4: Deep Learning

  • Introduction to Deep Learning
  • Introduction to Deep Learning II
  • Output Function, Loss Functions and Deep Networks
  • MNIST Dataset
  • Introduction to CNN
  • CNN on MNIST and Batch Normalisation
  • Batch Normalization II
  • Visualizing CNN
  • Object Detection
  • Introduction to NLP
  • Word2Vec, Glove and Intro to RNN
  • Recurrent Neural Networks
  • LSTM and GRU
  • LSTM and GRU II
  • Encoder Decoder
  • Introduction to CNN
  • CNN on MNIST and Batch Normalization
  • AlexNet, GoogleNet and ResNet
  • Object Detection
  • Recurrent Neural Networks
  • Code Files
  • Autoencoders
  • Transformers, BERT and GPT
  • UNet, RCNN and Transfer Learning
  • Machine Translation Implementation
  • Image Classification and Transfer Learning

  SQL and Tableau

  • Introduction
  • SQL Commands
  • Handling Null Values and Sub Queries
  • Introduction to Tableau
  • Tableau Day II
  • Tableau III
  • Tableau IV
  • SQL Revision Session