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Professional Edge Diploma

AI & Data Science

Professional Edge Diploma

AI & Data Science

Professional Edge Diploma

  • EMI Options
    EMI Options:
  • Certification:
  • Mode
    Live Online
  • Duration
    18 months / 72 weeks
  • Online Sessions
    Online Sessions:
    120 (90-120 min each) plus 3-month Internship
  • Efforts:
    4-6 hours / week
  • Classes
    Weekends / Weekdays
  • Assignments:
  • Quizzes:
  • Projects

About Program

Program Overview

The "Professional Edge Diploma" program in AI and Data Science is an 18-month comprehensive and immersive program designed to equip students with the theoretical knowledge and practical skills required to excel in the field of AI and Data Science.

Global Recognition: Graduates of the program will receive a "Professional Edge Diploma", recognized from MEDH and STEM, providing them with global recognition and credibility in the field.

Financial Aid and Flexible Payment Options: The program offers financing options to make it financially manageable for students, ensuring that financial constraints do not hinder their learning experience.

Job Guarantee: Upon successful completion of the program, students are guaranteed a job, providing them with a clear pathway to kick-start their career in AI and Data Science.


Comprehensive Curriculum: The program covers a wide range of AI and Data Science topics, including but not limited to machine learning, deep learning, generative AI, data analysis, model training and evaluation, neural networking, and supervised and unsupervised learning.

Industry-Relevant Tools and Software: Students will gain hands-on experience with industry-standard tools and software used in the field of AI and Data Science, such as Python, pandas, NumPy, SciPy, and Generative Pretrained Transformers (GPT).

Practical Learning: The program emphasizes practical learning through real-world projects, allowing students to apply their skills to industry-specific use cases such as retail sales analysis, HR analytics, and financial services-related projects.

Professional Development: Throughout the program as well as post completion, students will receive mentorship, engaging speaker series with industry leaders, and support for their professional growth and development.

Cutting-Edge Curriculum: The program places a strong emphasis on Generative AI, enabling students to delve into advanced concepts such as variational autoencoders, generative adversarial networks (GANs), diffusion models, and transformers.

Preparation for Future Careers: Graduates of the program will be equipped with job-ready skills to pursue Assured-Job-Roles in Corporate such as Data Scientist, Machine Learning Engineer, AI Engineer, and more, preparing them for the evolving landscape of AI and Data Science careers.

In nutshell, the "Professional Edge Diploma" program in AI and Data Science from MEDH offers a comprehensive, industry-relevant, and immersive learning experience, providing students with the knowledge, skills, and practical exposure necessary to excel in the field of AI and Data Science, and ensuring a clear pathway to career success.

3 Month Internship

- Full-time Internship (Work-from-Office or Work-from-Home), based on Feasibility.
- Internship Reputable Industry Organizations or Institutions would be provided by MEDH, though the student     shall be given an option to join at any place of his/her choice, subject to the prior approval.
- Hands-on Experience in AI, Machine Learning, Data Analysis, or Related Areas
- Exposure to Real Industry Challenges and Projects
- Opportunity to Work on Live Projects and Contribute to the Organization
Mentorship and Guidance from Experienced Professionals

Capstone Project:

Duration: Concurrent with the internship period
- Application of AI and Data Science Skills to Solve a Real-world Problem or Develop an Innovative Solution
- Guided by Program Instructors and Industry Mentors
- In-depth Exploration of the Chosen Area of Interest
- Development of a Comprehensive Project Report and Presentation
- Emphasis on Innovation, Creativity, and Practical Implementation
- Showcasing the Ability to Address Complex Challenges using AI and Data Science techniques

The internship and capstone project(s) integration aims to bridge the gap between academic learning and real-world application, preparing students for successful careers in AI and data science. This practical experience will further solidify their understanding of industry practices and set the foundation for a successful transition into the professional landscape.


Duration:  18 Months Course (72 weeks/120 sessions of 90-120 minutes each) plus 3-month Internship

Chapters & Topics

Weeks 1-2: Introduction to AI and Data Science

– 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

Weeks 3-4: Python Fundamentals

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

Weeks 5-6: Python Data Structures

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

Weeks 7-8: Python Deep Dive

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

Weeks 9-10: Python Frameworks Library for Data Science & Analysis

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

Weeks 11-12: Data Analysis

– 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

Weeks 13-16: Data Visualization with Tableau

– 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

Weeks 17-20: Probability and Statistics

– 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

Weeks 21-24: Machine Learning (ML) Fundamentals

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

Weeks 25-28: Machine Learning (ML) Advanced

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

Weeks 29-32: Generative AI

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

Weeks 33-36: Deep Learning with Keras and TensorFlow

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

Weeks 37-39: Convolutional Neural Networks (CNNs)

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

Weeks 40-42: Recurrent Neural Networks (RNNs) & NLP

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

Weeks 43-45: Reinforcement Learning

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

Weeks 46-48: Big Data for Data Science

– 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

Bonus Module (Job / Professional Readiness)

– 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

(Detailed Coverage in Weeks 49-60)

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

Tools & Technologies You Will Learn in this Course,
Aligning with Industry Standards and Job Requirements

– Programming and Development Environments: Python, and Jupyter
– Data Manipulation and Analysis: NumPy, and Pandas
– Data Visualization: Matplotlib, and Tableau
– Databases: MongoDB
– Scientific Computing: SciPy
– Machine Learning and Deep Learning: Scikit-Learn, Keras, Tensorflow, and BERT
– Big Data and Distributed Computing: Hadoop, Spark, and Apache Kafka
– AI and Language Models: ChatGPT, Prompt Engineering, and LongChain
– Generative Models: Stable Diffusion, and DALL-E
– Model Deployment: RESTful APIs
– Cloud Platforms: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure

3-month Internship with Capstone Project

– 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

Assessment, Evaluation & Certification

– 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, Networking and Lifelong Learning

– 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

Note: This curriculum is subject to minor modifications based on the class progress and feedback. Each course is designed to incorporate a mix of interactive activities, case studies, role plays, and reflective exercises to cater to the specific needs and developmental milestones of the respective age group.

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