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Technology For All

PROGRAM OVERVIEW

CURRICULUM

  Welcome to Technology For All

  • Introduction to the course
  • Data Science Introduction

  Guest Lecture

  • Evolution of NLP
  • Day1 - PPT
  • Evolution of NLP - Day 2

  Module - 1: Python Programming

  • Introduction to Python (22nd Oct)
  • Python Introduction
  • Data Structures (25th Oct)
  • Python Notes
  • Data Structures II (26th Oct)
  • Operators (27th Oct)
  • Loops (28th Oct)
  • Control Statements (29th Oct)
  • Functions (1st Nov)
  • Functions II (2nd Nov)
  • Class and Object(8th Nov)
  • Mentoring Session(9th Nov)
  • OOPs (10th Nov)
  • Mentoring Session (15th Nov)
  • Mentoring Session (16th Nov)
  • Mentoring Session (17th Nov)
  • OOPs II (25th Nov)
  • Misc Concepts in Python (26th Nov)
  • Files of Python

  Module-1: Assignments

  • Simple Calculator
  • Intro to Python and Data Types Assignment
  • Data Types II and Decision Control Assingment
  • Loops Assignment
  • Functions Assignment
  • Common Modules in Python
  • Classes and Modules
  • Numpy Assignment
  • Pandas Assignment

  Module - 2 : Flask

  • Introduction to Flask
  • HTML Files with Flask
  • Jinja Templating
  • Template Inheritance
  • SQL Alchemy - ORM
  • Mentoring Session - 14th Nov
  • Flask Revision session-1(15th Dec)
  • Flask Revision Session II
  • Flask Revision Session III
  • Flask Files

  SQL and Tableau

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

  Internship Tasks

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

  Module - 3: Data Analysis

  • Introduction to Data Analysis I
  • Intro to Data Analysis II
  • NumPy
  • Mentoring Session
  • Array Object and Numerical Operations
  • Introduction to Pandas
  • Pandas II
  • Exploratory Data Analysis
  • Descriptive Statistics
  • Web Scraping
  • Web Scraping II
  • Pyplot
  • Plotly

  Module - 3: Assignments

  • Numpy Assignment
  • Pandas Assignmment
  • Visualization Assignment
  • Visualization Assignment

  Module - 4: Machine Learning

  • Introduction to Machine Learning
  • Overview of Machine Learning
  • Regression
  • ML Notes
  • Linear Regression
  • Logistic Regression
  • Linear Regression Hands on
  • Evaluation Metrics
  • Feature Engineering Notes
  • Model Performance Measures
  • Linear Regression - Hands on
  • Wine Dataset -live project
  • Model Performance Measures
  • Wine Quality Dataset
  • K-Nearest Neighbours
  • Naive Bayes
  • Decision Trees
  • Ensemble Techniques
  • Decision Trees
  • KNN Files
  • Unsupervised Learning
  • PCA

  Module 5: Deep Learning

  • Introduction to Neural Networks
  • Neural Networks with Fashion MNIST
  • Image Processing
  • CNN - Convolution Neural Networks
  • VGG Net
  • Bag of Words
  • Recommender System
  • RNN
  • RNN Implementation
  • CNN Material
  • RNN Material
  • ANN Material

  Revision Sessions

  • Python Revision Session

  Module 4: Assignments

  • Linear Regression Assignment
  • Logistic Regression Assignment
  • KNN Assignment
  • Decision Tree Assignment

  Placement Activities

  • Resume Building and Interview Preparation Session

  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