-
Introduction to the course
-
Data Science Introduction
Tutors
Technology For All
PROGRAM OVERVIEW
CURRICULUM
Welcome to Technology For All
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
-
SQL Revision Session
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