• Introduction to Artificial Intelligence, Machine Learning, Deep Learning

• Relationship between AI,ML,DL and Data Science

• Introduction to Data Science

  What is Data Science

  Why Data Science

• Components of data Science

• Applications of data science

• Why Python for Data Science

• Installation of Anaconda Python
• Variables, Standard Data Types, Basic Operators
• Functions, Modules,
• String,
• List, Tuple,
• Dictionary, Set
• List comprehension, Dictionary comprehension. Set Comprehension • Regular expressions
• Python File I/O
• Database Operations using Sqlite
• Generators, Iterators, Decorators.
• Collections Module
• Object Oriented Python,

• Class, Object
• Abstraction, Encapsulation, Inheritance, Polymorphism

• HTML. CSS, Web Scraping, Flask web application Framework

• Central Tendency (mean, median and mode)
• Measures of Variation (Interquartile Range, Variance, Standard Deviation)
• Bar Chart, Histogram, Box whisker plot, Scatter Plot Co-variance, Correlation
• Binomial Distribution, Normal Distribution
• Skewness and Kurtosis

• Central Limit Theorem

• Z Test, T Test, P-value

• Hypothesis testing

• Chi-Square, F- Statistic

• Sampling Techniques

• Introduction to NumPy, N-D array

• Mathematical Functions of NumPy

• Data types and attributes of Arrays

• Array Indexing and Slicing

• 1-D arrays and N-D arrays
• Array broadcasting

• Introduction to Pandas,
• Understanding DataFrame
• Transforming List, Tuple, Dictionaries into Data Frame
• Accessing rows and columns, Iteration over Data Frames
• Pandas joining and merging,
• Pandas Groupby, Pivot Table, Binning,
• Pandas Visualization
• Data Generation
• Real time Case Studies on Data Analysis based on Kaggle

• Univariant analysis

• Bivariant analysis

• MatplotLib

Dimensionality Reduction Techniques

• PCA- Principle Component Analysis

• SVD- Singular Value Decomposition

• LDA- Linear Discriminant analysis

• What is regression, Linear regression

• Linear regression, Multiple Regression

• Ridge Regression, Lasso Regression

• Explanation of statistics

• Evaluation metrics (R-Squre, Adj R-Squre, MSE, RMSE)

• Train/Test Split, Hypothesis testing formal way

• Case Studites

• Project on Regression Analysis from Kaggle

• Introduction to Machine Learning

• Naïve Bayes classifier
• Decision Tree classifier
• Support Vector Machines

• KNN classifier
• Logistic Regression

• Support Vector Machines (SVM)
• Ensemble models (Random Forest, Bagging, Boosting)

• Xgboost indepth with Industry cases

• Backpropagation using Neural Networks

• SK Learn ML library using Python

• Case Studites

• Project on Classification Algorithms from Kaggle

• Accuracy measurements

• Precision, Recall, Precision – Recall Tread-off

• AUC Score, ROC Curve

  • Train/Validation/Test split, K-Fold Cross Validation

  • The Problem of Over-fitting (Bias-Variance tread-off)

• Bias, Variance, Overfitting, Underfitting methods

• Pipelining

• Parameter Tuning mechanisms (Grid Search, Random Search)

• Debugging algorithms with learning and validation curves

• Deployment of models into Flask

• Association Rules & Interesting measures

• Apriori Algorithm

• FP-Grouth algorithm

• Case Studites

• Similarity distance measures

• K-means Clustering

• Hierarchical Clustering

• DB Scan Clustering

• Case Studites

• NLP Overview, Applications using NLTK, Text Blob

• Tokenizing, Stop Word Removal, Stemming, Lemmatization, POS Tagging,

• Similarity measures over Text,
  • Vector Space Model, Bag of words,
  o transforming text to Numeric using Count Vectorizer • Text Classification,
  o Text Clustering,
  o Topic Modelling,
  o Model Deployment using NLP

• Word Embeddings, Sentiment Analysis

• • Case Studies

  • Project on NLP from Kaggle

• Image Processing Basics and Computer Vision Library

• Images Operations using Numpy

• Blending and Pasting

• Image Threshold

• Morphological Operations

• Edge detection

• Contour detection

• Feature Marching

• Face detection

• Introduction to Deep Learning, Neurons, Perceptron, Multilayer Perceptron,

• Forward Propagation, Backward Propagation, Activation Functions,

• Artificial Neural Networks, Case Studies

• Convolution Neural Networks, Case Studies

• Recurrence Neural Networks, Case Studies

• Model Deployment using Deep Learning

• Deep Learning with Text

• Word Embedding

• Transformers using HuggingFaces

o Encoders and Decoders o Attention mechanism

• Time Series Analysis

• Recommendation Systems (Filter based RS and Collaborative based RS)

• Saving Models, Deployment of models into Flask

• Industrial Case Studies

• Need of Explainable AI in Industry

• SHAP

• LIME

• Industrial Case Studies

• Gen AI text

• Gen AI images

• Retrieval-Augmented Generation

• Case Studies on Gen AI and Other

Features of the course

• Case Study based Teaching

• Industrial persons involve in Industrial case studies

• Interview based training