Professional Certification Program in AI & Data Science from DRISHTI CPS, IIT Indore

Data Handling with NumPy:

NumPy Arrays, CRUD Operations, etc.

Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.

Data Manipulation Using Pandas:

Loading the data, dataframes, series, CRUD operations, splitting the data, etc.

Data Preprocessing:

Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.

Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross validation techniques, train-test split, etc.

Data Visualization: Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc. with Python matplotlib. Regression plots, categorical plots, area plots, etc, with Python seaborn.

SQL Basics –

Fundamentals of Structured Query Language
SQL Tables, Joins, Variables

Advanced SQL –

SQL Functions, Subqueries, Rules, Views
Nested Queries, string functions, pattern matching
Mathematical functions, Date-time functions, etc.
Deep Dive into User Defined Functions

Types of UDFs, Inline table value, multi-statement table.
Stored procedures, rank function, SQL ROLLUP, etc.
SQL Optimization and Performance

Record grouping, searching, sorting, etc.
Clustered indexes, common table expressions.

Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.

Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.

Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.

Random Forest – Creating random forest models for classification problems in a supervised learning approach.

K-Nearest Neighbors – A simple algorithm that can be used for classification problems.

Performance Metrics

Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.

Confusion matrix – To evaluate the true positive/negative, false positive/negative outcomes in the model.

r2, adjusted r2, mean squared error, etc.

K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.

Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.

Artificial Intelligence Basics

Introduction to keras API and tensorflow
Neural Networks

Single Cell (perceptron)
Multi cell perceptron Topology
Weights & Biases
Build a NN from scratch (using numpy)
Deep Learning

Use cases of DL in industry
Difference between DS, ML, DL & AI
Lifecycle of Deep Learning Project

Text Mining, Cleaning, and Pre-processing

Various Tokenizers, Tokenization, Frequency Distribution, Stemming, POS Tagging, Lemmatization, Bigrams, Trigrams & Ngrams, Lemmatization, Entity Recognition.
Text classification, NLTK, sentiment analysis, etc

Overview of Machine Learning, Words, Term Frequency, Countvectorizer, Inverse Document Frequency, Text conversion, Confusion Matrix, Naive Bayes Classifier.
Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling

Language Modeling, Sequence Tagging, Sequence Tasks, Predicting Sequence of Tags, Syntax Trees, Context-Free Grammars, Chunking, Automatic Paraphrasing of Texts, Chinking.

AI Chatbots and Recommendations Engine

Using the NLP concepts, build a recommendation engine and an AI chatbot assistant using AI.

RBM and DBNs & Variational AutoEncoder

Introduction rbm and autoencoders
Deploying rbm for deep neural networks, using rbm for collaborative filtering
Autoencoders features and applications of autoencoders.
Object Detection using Convolutional Neural Net

Constructing a convolutional neural network using TensorFlow
Convolutional, dense, and pooling layers of CNNs
Filtering images based on user queries
Generating images with Neural Style and Working with Deep Generative Models

Mapping the human mind with deep neural networks (dnns)
Several building blocks of artificial neural networks (anns)
The architecture of dnn and its building blocks
Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.

Deploying deep learning models in Serverless Environments
Deploying Model to Sage Maker
Explain Tensorflow Lite Train and deploy a CNN model with TensorFlow

LSTM – What is LSTM?, How does LSTM work, Applications of LSTM, etc.

Transformers – What are transformers, how does a transformer work in deep learning, applications of transformers, types of transformers, encoder-decoded, self-attention, etc.

BERT – Language Models, What is BERT, How does BERT work, how is BERT different from LSTM, applications of BERT, etc.

GPT – What are generative pre-trained models (GPT), how does a GPT work?, real-life examples of GPT, etc.

LLM – NLP and Language models, what are LLMs, how does an LLM work, applications of LLM, etc.

VAEs – Introduction to Variational Autoencoders, Architecture of VAEs, creating VAEs for image generation.

Langchain – Intuition for Langchain, Langchain applications, Langchain architecture, How to work with Langchains, etc.

Prompt Engineering – Science behind Prompts, Prompt Engineering Basics, Impact and usage of Prompt Engineering, Prompt Engineering tools, Effectiveness of Prompts, etc.

Image-Based Applications of Generative AI – Image-based workflows and artifacts using Generative AI with image generation, text-image generation, etc.

Text-Based Applications of Generative AI – Leveraging Text summarization and text generation for text-based applications using Generative AI tools.

Audio-Based Applications of Generative AI – Text to Audio generation, Audio processing, Audio generation leveraging Generative AI tools to create end-to-end applications.