Module-1
Introduction
to AI and
Programming
Tools
Linux basics
Python Basics Data Types,
Conditional Statements,
Looping, Control
Statements, String, List
And Dictionary
Manipulations, Python
Functions, Modules And
Packages, Object Oriented
Programming in Python,
Regular Expressions,
Exception Handling,
Popular python packages
like pandas for data
handling
Introduction to Database
Management System &
SQL, Database Interaction
in Python.
Data Analysis &
visualization – using
numpy, matplotlib, scipy
R Programming:- Basics -
Vectors, Factors, Lists,
Matrices, Arrays, Data
Frames, Reading data.
Data visualization - barplot
pie, scatterplot, histogram,
scatter matrix
Probability and
StatisticsProbability,
Mean, Median,
SD, Variance, Probability
distributions in R- Normal
distribution, Poisson
distribution, Binomial
distribution. Correlation
and Regression
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Module 2-
Machine
Learning
Structured and unstructured
data handling
Data Preprocessing
Handling missing data
Data Standardization
Label Encoding
One hot encoding
Supervised and
Unsupervised Learning
Classification, Regression &
Clustering
Linear Algebra
Machine Learning
Algorithms
Linear Regression
KNN,
K Means,
Logistic Regression
Support Vector
Machine
Decision Tree
Naïve Bayes, etc.
Ensemble Methods
-
Random Forest, Boosting
and Optimization, etc.
Model Evaluation Metrics
Module -3
Deep
Learning and
Natural
Deep Learning Concepts
Artificial Neural Network
Deep Neural Networks
Convolutional Neural
etwork,Recurrent Neural,Network
OpenCV, Tensorflow,Keras
Introduction to Generative Adversarial
Networks(GAN)
Natural Language Processing Methods
Basics of text processing Lexical processing
Syntax and Semantics, Parts of Speech,Tagging
Applications like Sentiment Analysis,
Text Classification,Text Summarization,
Document Clustering,Document Similarity,
Web Crawling etc.
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