– Scope and Significance of AI and Data Science Across Diverse Industries
– Distinctions between AI Engineers, Software Engineers, and Data Scientists
– Future in AI, Machine learning, and Data Science
– Generative AI, LLM (Large Language Models), and Image Generation

– Data Types & Operators, Control Structures – If-Elif Statement
– Control Structures – For Loop
– Control Structures – While loop
– String Functions and Operations

– Comprehensive Study of Lists and their Function
– Understanding Tuples and their Functionality
– Exploring Dictionary and its Functions
– Leveraging Sets for Unique Data Handling

– Mastering Python Functions and their Application
– Understanding Functional Arguments and their Implementations
– Learning Robust Error Handling Techniques
– Understanding Regular Expressions (Regex) for Pattern Matching

– Introduction to NumPy: Numeric Computing with Python
– Exploring NumPy Broadcasting for Efficient Array Operations
– Introduction to Pandas
– Pandas Functionality for Data Analysis and Manipulation

– Data Storytelling with Matplotlib
– Exploratory Data Analysis (EDA) Techniques and Approaches
– Exploring Databases, Different Models and Use Cases
– Understanding NoSQL Databases and MongoDB, and its Benefits in Data Analysis

– Introduction to Tableau and Data Visualization Techniques (Charts, Heat Maps, Tree Maps, and Box Plots)
– Interactive Dashboards, Compelling Data Stories, Blending and Joining
– Advanced Analytics and Forecasting (Trend Lines, Clustering, and Predictive Modeling etc.)
– Recap, Project, Assessment and Certification

– Probability and Types of Events
– Types of Statistics (Descriptive & Inferential); Types of Data (Qualitative, Qunatitative, & Outliers)
– Measure of Central Tendency – Mean, Mode, Median,
– Measure of Spread – Range, Variance, Standard Deviation and IQR, Hypothesis Testing

– Introduction to Machine Learning
– Types of Machine Learning: Supervised, Unsupervised, and Reinforcement
– Linear Regression
– Logistic Regression

– Evaluation Metrics
– Decision Trees, Random Forests
– Support Vector Machines (SVM)
– Dimentionality Reduction using Principal Component Analysis (PCA)

– Core Principles of Generative AI, Prompt Engineering and ChatGPT
– Large Language Models (LLM)
– Generative Adversarial Networks (GANs)
– Recap, Project, Assessment and Certification

– Introduction to Neural Networks, Activation functions
– Backpropagation, Training neural networks
– Introduction to TensorFlow, Model Optimization
– Logging training metrics in Keras

– Understanding the Architecture of CNNs
– Image Recognition and Classification using CNNs
– Transfer Learning with Pre-trained Models

– Exploring the Concepts of RNNs and their Applications
– Implementing RNNs in NLP and Web Scraping
– Text Preprocessing and Sentiment Analysis using RNNs

– Introduction to Reinforcement Learning and its Applications
– Understanding Markov Decision Processes (MDP)
– Implementing Q-learning and Deep Q Networks (DQNs)

– Introduction to Big Data Technologies (Hadoop and Spark)
– RESTful APIs for Model Deployment
– Hands-on experience with Cloud Platforms like AWS, GCP, or Azure
– Recap, Project, Assessment and Certification

– Soft Skills Development
– Networking strategies and building a professional online presence
– Address Specific Career Goals and Valuable Advice for Navigating the Job Market
– Professional Resume and Interview Preperation
– Job Assistance through Medh Placement Cell

Elective – 1: Harnessing Generative AI for Textual Innovation
– Training RNNs for Language Modeling and Text Generation (LLM)
– GANs for Text Generation, Encoder-Decoder Models for Text Generation
– Sentiment Analysis and Opinion Mining
– Named Entity Recognition and Information Extraction, Sentiment Analysis
– Attention Mechanisms for Improving Sequence-to-Sequence Models
– Text Summarization and Text Generation
– Transformer Models for Text Generation, Pre-trained Transformer Models (e.g., GPT, BERT) for Text Generation and Fine-tuning
– End-to-End Capstone Project and Deployment Considerations

Elective – 2: Mastering Generative AI for Image Exploration
– Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)
– Image Generation with GANs: DCGAN, StyleGAN, Stable Diffusion a Text-to-image Diffusion Model
– Image Segmentation and Feature Extraction
– Deep Learning for Image Classification and Localization
– Generative Adversarial Networks (GANs) for Image Synthesis
– Generative Models for Image Denoising and Restoration
– Real-world Applications of Image Super-resolution and Enhancement, Use Cases – Dall-E
– End-to-End Capstone Project and Deployment Considerations

Elective – 3: Big Data Analytics and Distributed Computing
– Feature Engineering and Dimensionality Reduction for Big Datasets
– Data Processing with Spark RDDs and DataFrames
– Spark SQL and Data Analysis
– Machine Learning with Spark MLlib
– Real-time Stream Processing with Apache Kafka and Spark Streaming
– Data Visualization and Dashboards for Big Data Analytics
– Hands-on Experience with Cloud Platforms (AWS, GCP, or Azure) for Big Data
– End-to-End Capstone Project and Deployment Considerations

– Real-world Experience in a Corporate Setting
– Application of Skills Learned during the Program
– Regular Mentorship and Progress Reviews by Industry Experts
– Recap, Assessment and Final Certification

Guaranteed Employment or Your Program Fee Will Be Refunded

– Weekly Quizzes to Gauge Comprehension of Key Concepts
– Practical Hands-on Assignments and Thorough Evaluation
– Active Engagement in Group Discussions
– Capstone Project
– Certification Upon Program Completion

– Medh Alumini Status and Networking Opportunities
– Access to an Extensive ‘Medh Alumni Network’ for Professional Connections and Mentorship
– Career Advancement Resources and Job Opportunities within the ‘Medh Alumni Community’
– Continued Learning through ‘Medh-Alumni-Exclusive’ Webinars and Industry Insights
– Networking Events to Foster Connections with Fellow Alumni and Industry Professionals