Post Graduate Program In Data Science with Artificial Intelligence

1.1 Introduction to Excel for Data Science - Interface, Ribbon, Shortcuts, Navigation, Formatting Data, Number Formats, Conditional Formatting, Basic Data Entry, Sorting, and Filtering
1.2 Basic Mathematical Functions - SUM, AVERAGE, COUNT, MIN, MAX, ABS, ROUND, etc.
1.3 Data Cleaning & Preprocessing - Handling Missing Data, Removing Duplicates & Data Validation, and Text Functions (LEFT, RIGHT, MID, LEN, TRIM, UPPER, LOWER, PROPER, etc.)
1.4 Conditional Logic & Lookup Functions - IF, IFERROR, AND, OR, VLOOKUP, HLOOKUP, XLOOKUP, INDEX, MATCH, etc.
1.5 Basic Visualization - Bar Chart, Line Chart, Pie Chart, etc. 1.6 Introduction to AI-powered Excel Tools (Copilot, AI Insights, etc.)

2.1 Pivot Tables & Data Aggregation - Creating Pivot Tables & Pivot Charts, Advanced Filtering with Slicers & Timelines, Grouping Data, Calculated Fields & Custom Aggregations
2.2 Advanced Statistical & Logical Functions - COUNTIF, SUMIF, AVERAGEIF, STDEV, VAR, CORREL, RANK, PERCENTILE, FREQUENCY, TREND, LINEST, GROWTH
2.3 Automating Data Cleaning with Power Query - Importing Data from CSV, SQL, and Web. Merging & Appending Datasets. Cleaning & Transforming Data for Analysis.
2.4 AI Powered Data Analysis - AI Powered Insights, Automating AI-driven Data Cleaning, AI Generated Forecasting & Trend Analysis

1) Introduction to Python - print(), input(), Comments, Variables, Built-in Data Types
2) Basics of Python - Strings and its methods, Python Booleans, Operators (Arithmetic, Logical, Comparison, Assignment, Identity, Memebership, Bitwise), Slicing and Indexing

1 NumPy - Numerical Python
1.1 Introduction - Arrays (Indexing, Dimensions, Slicing, Shape, Reshape, Iteration, Joins, Split, Search, Sort, Filter, Copy Vs View
1.2 Random - Introduction, Pseudo vs True Random, Shuffle and Permutations, Random Distributions (Poisson, Normal, Binomial, etc.)
1.3 Universal Functions - Simple Arithmetic, Rounding Decimals, Log, Summation, Product, Differences, LCM, GCD, Set Operations, Trigonometric and Hyperbolic Functions
2 Data Wrangling using PANDAS 
2.1 Introduction to Pandas - Installation (using PIP) and Import
2.2 Pandas Series and DataFrames - Difference, Labels, Key/Value Objects
2.3 Pandas DataFrames Tutorial - View, Info, Description, Location of Rows and Columns, Named Indexes, CSV/EXCEL Files
2.4 Pandas DataFrames Advanced Tutorial - Cleaning of Data, Handling Null Values (Dropna/Fillna), Handling Duplicate Values, Handling Data in Wrong Format
2.5 Plotting of Data Using Pandas 
3 Data Visualization Using Matplotlib and Seaborn
3.1 Introduction to Data Visualization - Installation (using PIP) and import of Matplotlib and Seaborn, Basics of Plotting (Labels, X Axis, Y Axis, Headings)
3.2 Matplotlib and Seaborn - Line Plots, Bar Graphs, Scatter plots, Pie Charts, Heatmaps for Correlation, etc.
3.3 Exploring Outliers and Data Distributions - Boxplots, Histograms, KDE Plot, Violin Plot, Pair Plot, etc.
3.4 Advanced Plotting Tutorial - Adding a third Axis on a 2D plot, Fonts, Styles, Subplots, Axis, Grids, Texts on Plots 

2.1 Installation, Setup, Importing CSV and Excel Files, Connecting SQL Databases and Cloud Services
2.2 Data Cleaning and Wrangling - Handling Missing Values, Handling Duplicates, Formatting of Data, Joins in Tableau
2.3 Basic Visualizations - Bar Chart, Line Chart, Pie Chart, Scatter Plot, Geographical Data Visualization on Maps, Dashboards in Tableau
2.4 Advanced Visualizations - Heat Maps, Tree Maps, Boxplots, Histograms, Parameters for interactivity and flexibility, Calculated Fields to create new metrics and dimensions
2.5 Analytics and Statistical Tools - Trend Lines and Forecasting, Clustering Techniques and Distribution Analysis
2.6 Filters, Highlighters, Actions to create interactive dashboards, Dashboard Designs
2.7 Case Study - Real World use case of Tableau for Data Science

3.1 Installation, Setup, Importing Files, Connecting SQL Databases and Cloud Services, Direct Query Methods in Power BI
3.2 Power Query - Data Cleaning and Transformation (Handling Missing Values and Duplicates, Formatting of Data)
3.3 Data Modeling - Calculated Columns, Managing Data Models, Creating Relationship between two tables
3.4 Basic and Advanced Data Analysis Expressions (DAX) Tutorial
3.5 Basic and Advanced Visualizations - (Basic - Bar Charts, Pie Charts, Matrices, Maps, etc.), (Advanced - Custom Visualizations, Slicers, Filters, Waterfall Charts, Funnel Charts, Gauge Charts, etc.)
3.6 Automated Quick Insights and AI Visuals, Dashboard Designs in Power BI
3.7 Case Study - Real World use case of Power BI for Data Science 

2.1 Descriptive Statistics - Estimates of Location (Mean, Weighted Mean, Trimmed Mean, Median, Weighted Median, Mode, Outliers), Estimates of Variability (Deviations, Variance, Standard Deviation, Mean Absolute Deviation, Median Absolute Deviation, Range, Percentiles, Quantiles, Deciles, Interquartile Range, Degrees of Freedom), Skewness and Kurtosis
2.2 Sampling Techniques - Bias Sample, Population, Random Sampling, Stratified Sampling, Simple Random Sampling, Bootstrap, Resampling
2.3 Inferential Statistics - Confidence Intervals, Normal Distribution (Z-score, QQ-Plot), T-Distribution and T-test, Binomial Distribution, Chi-Square Distribution and Chi-Square Test, F-Distribution, F-test, ANOVA Test, Poisson Distribution, Exponential Distribution, Weibull Distribution
2.4 Correlation Coefficient, Coefficient of Determination, Simple Linear Regression in Statistics 

3.1 Performance Metrics - Accuracy, Recall, Precision, F1 Score, Confusion Matrix, Classification Report, Precision/Recall Tradeoff, ROC Curve, AOC Curve
3.2 Classification Models - Gradient Descent and Stochastic Gradient Descent, Logistic Regression, K Nearest Neighbors (KNN), Naive Bayes, Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Decision Trees
3.3 Ensembling Methods - Bagging (Voting Classifier, Cross Validation, etc.), Boosting (XG Boost, Adaboost, etc.), Random Forest Classifier, Stacking
3.4 Advanced Techniques - Hyperparameter Tuning, GridSearchCV, RandomizedSearchCV, Multilabel Classification, L1 and L2 Regularization for overfitting, Handling Class Imabalance
3.5 Classification Project - Real-World Use Case

4.1 Introduction - Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Cost Function and Gradient Descent
4.2 Performance Metrics - Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, etc.
4.3 Challenges - Heteroskedasticity, Non-Normality of Data, Multicollinearity of Data, etc.
4.4 Regression Models - Decision Tree Regressor, Support Vector Machine (SVM), K Nearest Neighbors (KNN)
4.5 Ensemble Models - Cross Validation, Voting Classifier, Random Forest, Bagging and Boosting Methods
4.6 Advanced Techniques - Hyperparameter Tuning, GridSearchCV, RandomizedSearchCV, L1 and L2 Regularization
4.7 Regression Project - Real-World Use Case

5.1 Introduction to Unsupervised Learning
5.2 Clustering Methods - KMeans, Hierarchical, Model-Based Clustering, DBSCAN Clustering, Anomaly Detection using Gaussian Mixture Models
5.3 Dimensionality Reduction Using Principal Component Analysis
5.4 Building and Working of Recommendation Engines

1.1 Biological to Artificial Neurons
1.2 The perceptron
1.3 Multi-layer Perceptrons (MLPs)
1.4 Input Layer, Hidden Layers and Output Layers
1.5 Weights and Biases
1.6 Regression MLPs
1.7 Classification MLPs
1.8 Activation Functions and Optimizers 

2.1 Building a Neural Network using Sequential API
2.2 Building a Neural Network using Functional API
2.3 Building a Neural Network using Subclassing API
2.4 Saving and Restoring a Model
2.5 Callbacks 

3.1 Vanishing/Exploding Gradients Problem
3.2 Batch Normalization
3.3 Gradient Clipping
3.4 Transfer Learning - Using Pretrained Layers
3.5 Pretraining on Auxiliary Task
3.6 Faster Optimizers - RMSprop, AdaGrad, Adam, Nadam, Nesterov Accelerated Gradient
3.7 Learning Rate Scheduling 

4.1 How to choose number of hidden layers and number of Neurons
4.2 Learning Rate, Optimizer, Batch Size, Loss Functions and Activation Functions
4.3 L1 and L2 Regularization
4.4 Dropouts and Batch Normalization
4.5 Max Norm Regularization

2.1 Core Concepts - Hidden State, Back Propagation through time, Challenges (Vanishing/Exploding Gradients, Short-term Memory)
2.2 Basic Architectures (Simple and Deep RNNs), Advanced Architectures (Long Short-Term Memory, Gated Recurrent Units), Bidirectional RNNs, Sequence to Sequence Models)
2.3 RNN Variants - Attention Mechanisms in RNNs, Conditional RNNs, Hierarchical RNNs)
2.4 Incorporate Transformers, Hybrid Models (Combination of RNNs with CNNs and Attention Mechanisms for Generative AI)
2.5 Applications - Text Generation, Music Composition, Speech Synthesis, Video Generation, Language Translation

3.1 Architecture - Encoder/Decoder Structure, Self Attention Mechanism, Positional Encoding, Residual Connections, Training of Transformer Models
3.2 Variants of Transformers - Encoder Only, Decoder Only, Encoder-Decoder, Vision Transformers, Multimodel Transformers, Efficient Transformers
3.3 Attention Mechanisms - Soft, Hard, Sparse, and Cross Attention Mechanisms
3.4 Fine Tuning and Transfer Learning - Prompt Engineering, Few-shot and Zero-shot Learning, LoRA (Low Rank Adaptation)
3.5 Transformer Models for Text Generation - BERT, GPT (2,3,4), BART, CLIP
3.6 Relevance for Generative AI - Autoregressive Modelling, Masked Language Modelling, Sequence to Sequence Models, Reinforcement Learning with Human Feedback (RLHF)

1. NLP Fundamentals - Introduction to NLP (Definition & Importance), Applications (Chatbots, Sentiment Analysis, Machine Translation, etc.)
2. Text Processing - Tokenization (Word & Sentence), Stopword Removal, Stemming & Lemmatization, Part-of-Speech (POS) Tagging, Named Entity Recognition (NER), Text Normalization (Lowercasing, Removing Special Characters, etc.)
3. Word Representation (One Hot Encoding, Bag of Words, Term Frequency - Inverse Document Frequency [TF-IDF], Word Embeddings (Word2Vec, GloVe, FastText)
4. Popular NLP Models - BERT, GPT, T5, ZLNet, CLIP, Fine Tuning Pretrained Models
5. Text Generation - Autoregressive Models (GPT), Masked Language Models (BERT), Summarization (Extractive & Abstractive)
6. Speech-to-Text & Text-to-Speech - Automatic Speech Recognition (ASR), Tacotron, WaveNet, FastSpeech
7. Conversational AI & Chatbots - Rule-Based Chatbots, Retrieval-Based Chatbots, Generative Chatbots 8. Miscellaneous - Model Compression & Quantization, OpenAI API, Ethics & Bias in NLP (Fairness in AI & Reducing Bias in Language Models 

1.1 Relevance of Generative AI - Dimensionality Reduction, Data Denoising, Anomaly Detection, Image Generation, Feature Extraction, Latent Space Manipulation, Data Generation
1.2 Training of Autoencoders, Architecture - Encoder, Decoder and Latent Space (Bottleneck)
1.3 Types of Autoencoders - Vanilla Autoencoders, Denoising Autoencoders, Sparse Autoencoders, Convolutional Autoencoders, Variational Autoencoders, Contractive Autoencoders, Stacked Autoencoders, Adversarial Autoencoders
1.4 Advanced Architectures - Beta-VAE, Conditional Autoencoder, Sequence to Sequence Autoencoder, and Graph Autoencoder

2.1 Applications of GANs in Generative AI - Image Generation, Video Generation, Text to Image Synthesis, Music and Audio Generation, Style Transfer
2.2 Architecture - Generator, Discriminator, Adversarial Loss
2.3 Types of GANs - Vanilla GANs, Deep Convolutional GANs, Conditional GANs, Wasserstein GANs, Progressive Growing GANs, Cycle GANs, Style GANs, BigGANs, Pix2Pix.
2.4 Challenges - Mode Collapse, Non-Convergence, Vanishing Gradients
2.5 Advanced Concepts (Attention GANs, 3D GANs, Speech GANs, Multi-Model GANs), Metrics (Inception Score, Fréchet Inception Distance, Perceptual Path Length)
2.6 Metrics - Inception Score, Fréchet Inception Distance, Perceptual Path Length
2.7 Key Differences between Autoencoders and General Adversarial Networks 

3.1 Generative AI in NLP - Text Generation (GPT Models), Code Generation (Codex - Github Copilot, Code Llama), Chatbots: RAG (Retrieval-Augmented Generation), Fine Tuning LLMs
3.2 Generative AI in Computer Vision - Text-to-Image (DALL-E, Midjourney, Stable Diffusion), Image Super-Resolution (ESRGAN), Deepfake Generation (Face Swap, Video Synthesis)
3.3 Generative AI in Music & Audio - WaveNet (Google's Speech Synthesis), Jukebox (Open AI), Audio-to-Text Models (Whisper, DeepSpeech)
3.4 Generative AI in Healthcare & Science - Drug Discovery (AlphaFold), Medical Image Generation (AI-assisted X-ray / MRI Processing)
3.5 Generative AI in Finance & Business - Synthetic Data Generation, AI-Powered Content Creation

1.1 Answering complex user queries using Retrieval-Augmented Generation (RAG).
1.2 Generating high-quality images using a prompt.
1.3 Deploying the application for real-world use

2.1 Text Query Answering Module (using RAG with Transformers).
2.2 Creative Writing Module (text generation using GPT or custom Transformer models).
2.3 Image Generation Module (using Diffusion Models like Stable Diffusion or DALL·E).
2.4 Unified Frontend Interface for multi-modal interaction.
2.5 Backend API for serving models.
2.6 Deployment: Cloud-based or on-premise. 

3.1 Backend - FastAPI, Flask, Hugging Face Transformers, PyTorch, Tensorflow, OpenCV, Lancedb for Vector Search
3.2 Frontend - Gradio, Streamlight
3.3 Deployment - AWS, GCP, Azure, Docker, Kubernetes

4.1 A fully functional multi-modal AI assistant with text and image generation capabilities.
4.2 A deployed system accessible via a web interface.
4.3 A scalable architecture ready for real-world applications. 

5.1 Mastery of Retrieval-Augmented Generation (RAG) for text generation.
5.2 Hands-on experience with text-to-image generation.
5.3 Ability to fine-tune transformer models for creative writing and specific tasks.
5.4 Development of full-stack AI applications with backend and frontend integration.
5.5 Deployment of models using Docker and cloud platforms.
5.6 Knowledge of scalable AI systems with Kubernetes.
5.7 Practical experience in data preprocessing for text and image tasks.
5.8 Use of evaluation metrics for assessing generative models.
5.9 Documentation of systems and API integration for real-world applications.
5.10 Exposure to AI ethics, deployment best practices, and model security.

• 12 Month Complete Internship