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Technology For All

PROGRAM OVERVIEW

CURRICULUM

  Welcome to TechnologyForAll

  • Anaconda Installation File
  • Course Curriculum
  • Introduction to DataScience

  Module 1: Python Programming

  • Introduction to Python
  • Datatypes, Operators, and Strings
  • Day 1 and 2 Python files
  • Functions and understanding traceback logs
  • Strings and Lists
  • Revision Session
  • Functions - Lambda
  • Functions - Maps
  • Map, Reduce and Filter
  • Modules
  • OOPs Concepts
  • Classes and Inheritence
  • Encapsulation

  Module 1: Assignments

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

  Module 2: Data Analysis

  • Introduction to Data Analysis
  • Intro to Numpy
  • Numpy II
  • Introduction to Pandas
  • Pandas II
  • Pandas III
  • EDA
  • EDA II
  • Visualization
  • Seaborn Visualization
  • Web Scraping
  • Web Scraping II
  • Regular Expressions
  • Streamlit
  • Streamlit II

  Module 2: Assignments

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

  Internship Tasks

  • Web Scraping Project
  • Machine Learning Project 1
  • Machine Learning Project 2
  • Deep Learning Project

  Module 3: Machine Learning

  • Introduction to Machine Learning
  • Intro to Machine Learning II
  • Evaluation Metrics
  • Metrics PDF
  • Math Behind ML
  • Notes - Math behind ML
  • KNN Algorithm
  • Linear Regression
  • Linear Regression - Hands On
  • Logistic Regression Algorithm
  • Linear and Logistic
  • KNN File
  • Decision Tree PDF
  • Decision Trees
  • Logistic Regression PDF
  • Naive Bayes
  • Bagging Techniques
  • Support Vector Machines
  • Ada Boost
  • XG Boost Algorithm
  • K - Means Clustering
  • PCA

  Revision Sessions

  • Python Session
  • Python File

  Module 3: Assignments

  • Linear Regression Assignment
  • Logistic Regression Assignment
  • KNN Assignment
  • Decision Tree Assignment
  • Naive Bayes Assignment
  • Ensemble Methods Assignment
  • SVM Assignment
  • Accuracy Metrics Assignment

  Module 4: Deep Learning

  • Introduction to Deep Learning
  • Activation Functions
  • Activation Functions II
  • Cost Function
  • Revision Session
  • Optimization Function and Loss Functions
  • Back Propagation
  • Diabetes Classification
  • Diabetes Contd
  • Computer Vision
  • Image Processing
  • Convolution Neural Networks
  • CNN Implementation
  • VGG 16 vs Custom Model

  Placement Activities

  • Resume Building and Interview Preparation Session

  Deep Learning

  • Introduction to Deep Learning
  • Cost Functions and Activation Functions
  • Optimization Functions
  • House Price Prediction Dataset
  • Introduction to CNN
  • Iris Classification - Tensorboard
  • Intro to computer Vision. CNN, Convolution, Max pooling

  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