Master Certificate in Deep Learning and Generative AI

Build AI-driven solutions by mastering Python, SQL, data analysis, and visualization with Pandas and NumPy
Develop and deploy machine learning and deep learning models using TensorFlow, Keras, and PyTorch.
Learn generative AI, prompt engineering, and large language models like ChatGPT to transform data and drive predictive analytics.

Talk to an Advisor

Start Date

Open for enrollment

Start Date

Open for enrollment

Start Date

Open for enrollment

Program Duration

6 Months

Mon & Wed (18:00 - 21:00 hrs)

Program Duration

6 Months

Mon & Wed (18:00 - 21:00 hrs)

Program Duration

6 Months

Mon & Wed (18:00 - 21:00 hrs)

Learning Format

Intructor-Led (Live Project-Based Learning)

Individuals who want structure & accountability

Evenings, 2x per week (6-9 PM)

Live classes, group projects, custom labs, 1:1 support

Mentor-Guided (Self-Paced + Projects)

Motivated learners who want flexible learning with support

Flexible + weekly mentor
check-in

Guided labs, weekly mentor sessions, TA support, real projects

Self-Paced (Curated Path – Tech Pack)

Independent learners exploring a career path

Start Anytime

MCIT LMS curated path, one-time mentor onboarding, tools access

Intructor-Led (Live Project-Based Learning)

Individuals who want structure & accountability

Evenings, 2x per week (6-9 PM)

Live classes, group projects, custom labs, 1:1 support

Mentor-Guided (Self-Paced + Projects)

Motivated learners who want flexible learning with support

Flexible + weekly mentor check-in

Guided labs, weekly mentor sessions, TA support, real projects

Self-Paced (Curated Path – Tech Pack)

Independent learners exploring a career path

Start Anytime

MCIT LMS curated path, one-time mentor onboarding, tools access

Program Overview

Explore essential topics such as Python, machine learning, deep learning, NLP, data visualization, generative AI, explainable AI, and ChatGPT through an interactive learning approach. Gain immersive hands-on experience with expert-guided labs designed to deepen your understanding and build practical skills.

Course Outline

Programming Fundamentals

Develop core Python programming skills that serve as the foundation for your data science journey. Learn to work with procedural and object-oriented programming, loops, functions, and multi-threading to build scalable and efficient applications.

Concepts Learned:

Fundamentals of Python programming
Procedural and object-oriented principles
Data types, operators, and string functions
Loops, conditional statements, and variable scope
Working with Jupyter Notebook environments

Applied Data Science with Python

Deep Learning Advance topic

Advanced Generative AI – Image Generation

Building a Data Science Portfolio

Machine Learning and Deep Learning

Learn the core concepts of machine learning, including supervised and unsupervised models, the ML pipeline, and ensemble techniques. Gain hands-on experience with TensorFlow, Keras, and PyTorch.

Concepts Learned:

Supervised learning: regression and classification
Ensemble learning: bagging, boosting, stacking
Overfitting, underfitting, and model evaluation
Artificial and deep neural networks
Forward and backward propagation in DNNs
Hyperparameter tuning and performance optimization
Convolutional neural networks (CNNs) for vision tasks
Deep learning model implementation on TensorFlow and PyTorch.

SKILLS GAINED

……………………..

Deep Learning Advance topic

Build literacy in Generative AI by exploring core algorithms and their applications. Learn how LLMs, GANs, and VAEs power chatbots, content generation, and creative AI tools.

Concepts Learned:

Autoencoders and model interpretability
Variational Autoencoders (VAEs)
Generative Adversarial Networks (GANs)
Transformer models for NLP and Vision
Tools and frameworks for GenAI experimentation

SKILLS GAINED

……………………..

Advanced Generative AI – Image Generation

Explore advanced image generation capabilities in GenAI. Master stable diffusion, denoising, embedding systems, and contrastive learning to create high-quality and context-aware visuals.

Concepts Learned:

Stable Diffusion techniques
Denoising methods for clarity and coherence
Shared embedding systems for image representation
Contrastive learning for model performance
Autoencoders in generative image workflows

SKILLS GAINED

……………………..

Generative AI Governance

Understand the ethical, regulatory, and governance aspects of Generative AI. Learn how to integrate governance frameworks, manage risks, and ensure compliance in AI projects.

Concepts Learned:

Ethical frameworks and AI principles
Governance structures and best practices
Risk management in AI projects
Lifecycle governance for data and models
Future trends in AI regulation and compliance

SKILLS GAINED

……………………..

Building a Data Science Portfolio

Capstone projects are the best way to turn your learning into tangible results. Throughout this program, you’ll complete multiple hands-on projects that allow you to practice the full data science workflow—from collecting and cleaning data to building models, generating insights, and presenting your findings.

These projects are designed to help you:

Apply real-world skills in Python, data analysis, machine learning, and visualization
Solve practical business problems using data-driven approaches
Demonstrate your abilities to potential employers through a portfolio of completed work
Gain confidence in handling data projects independently

By the end of this module, you’ll have a portfolio of capstone projects that not only reflects your technical expertise but also shows your problem-solving skills and readiness for a professional data science role.

SKILLS GAINED

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SKILLS GAINED

……………………..

SKILLS GAINED

……………………..

SKILLS GAINED

……………………..

Framer is a web builder for creative pros. Be sure to check out framer.com to learn more.

SKILLS GAINED

……………………..

Programming Fundamentals

Concepts Learned:

Fundamentals of Python programming
Procedural and object-oriented principles
Data types, operators, and string functions
Loops, conditional statements, and variable scope
Working with Jupyter Notebook environments

SKILLS GAINED

……………………..

Machine Learning and Deep Learning

Framer is a web builder for creative pros. Be sure to check out framer.com to learn more.

SKILLS GAINED

……………………..

Applied Data Science with Python

Gain a strong foundation in data science using Python. Learn data preparation, statistical analysis, visualization, and model building through real-world scenarios.

Concepts Learned:

Core principles of data science and analytics
Python essentials for data analysis (strings, lists, Lambda functions)
Data manipulation and wrangling with Pandas
Data visualization using Matplotlib, Seaborn, and Plotly
End-to-end statistical analysis pipelines

SKILLS GAINED

……………………..

Generative AI Governance

Framer is a web builder for creative pros. Be sure to check out framer.com to learn more.

SKILLS GAINED

……………………..

Machine Learning and Deep Learning

Learn the core concepts of machine learning, including supervised and unsupervised models, the ML pipeline, and ensemble techniques. Gain hands-on experience with TensorFlow, Keras, and PyTorch.

Concepts Learned:

Supervised learning: regression and classification
Ensemble learning: bagging, boosting, stacking
Overfitting, underfitting, and model evaluation
Artificial and deep neural networks
Forward and backward propagation in DNNs
Hyperparameter tuning and performance optimization
Convolutional neural networks (CNNs) for vision tasks
Deep learning model implementation on TensorFlow and PyTorch.

SKILLS GAINED

……………………..

Deep Learning Advance topic

Build literacy in Generative AI by exploring core algorithms and their applications. Learn how LLMs, GANs, and VAEs power chatbots, content generation, and creative AI tools.

Concepts Learned:

Autoencoders and model interpretability
Variational Autoencoders (VAEs)
Generative Adversarial Networks (GANs)
Transformer models for NLP and Vision
Tools and frameworks for GenAI experimentation

SKILLS GAINED

……………………..

Advanced Generative AI – Image Generation

Explore advanced image generation capabilities in GenAI. Master stable diffusion, denoising, embedding systems, and contrastive learning to create high-quality and context-aware visuals.

Concepts Learned:

Stable Diffusion techniques
Denoising methods for clarity and coherence
Shared embedding systems for image representation
Contrastive learning for model performance
Autoencoders in generative image workflows

SKILLS GAINED

……………………..

Generative AI Governance

Understand the ethical, regulatory, and governance aspects of Generative AI. Learn how to integrate governance frameworks, manage risks, and ensure compliance in AI projects.

Concepts Learned:

Ethical frameworks and AI principles
Governance structures and best practices
Risk management in AI projects
Lifecycle governance for data and models
Future trends in AI regulation and compliance

SKILLS GAINED

……………………..

Building a Data Science Portfolio

These projects are designed to help you:

Apply real-world skills in Python, data analysis, machine learning, and visualization
Solve practical business problems using data-driven approaches
Demonstrate your abilities to potential employers through a portfolio of completed work
Gain confidence in handling data projects independently

SKILLS GAINED

……………………..

SKILLS GAINED

……………………..

SKILLS GAINED

……………………..

Programming Fundamentals

Concepts Learned:

Fundamentals of Python programming
Procedural and object-oriented principles
Data types, operators, and string functions
Loops, conditional statements, and variable scope
Working with Jupyter Notebook environments

Programming Fundamentals

Concepts Learned:

Fundamentals of Python programming
Procedural and object-oriented principles
Data types, operators, and string functions
Loops, conditional statements, and variable scope
Working with Jupyter Notebook environments

SKILLS GAINED

……………………..

Applied Data Science with Python

Machine Learning and Deep Learning

Deep Learning Advance topic

Applied Data Science with Python

Gain a strong foundation in data science using Python. Learn data preparation, statistical analysis, visualization, and model building through real-world scenarios.

Concepts Learned:

Core principles of data science and analytics
Python essentials for data analysis (strings, lists, Lambda functions)
Data manipulation and wrangling with Pandas
Data visualization using Matplotlib, Seaborn, and Plotly
End-to-end statistical analysis pipelines

SKILLS GAINED

……………………..

Advanced Generative AI – Image Generation

Generative AI Governance

Building a Data Science Portfolio

Machine Learning and Deep Learning

Learn the core concepts of machine learning, including supervised and unsupervised models, the ML pipeline, and ensemble techniques. Gain hands-on experience with TensorFlow, Keras, and PyTorch.

Concepts Learned:

Supervised learning: regression and classification
Ensemble learning: bagging, boosting, stacking
Overfitting, underfitting, and model evaluation
Artificial and deep neural networks
Forward and backward propagation in DNNs
Hyperparameter tuning and performance optimization
Convolutional neural networks (CNNs) for vision tasks
Deep learning model implementation on TensorFlow and PyTorch.

SKILLS GAINED

……………………..

Deep Learning Advance topic

Build literacy in Generative AI by exploring core algorithms and their applications. Learn how LLMs, GANs, and VAEs power chatbots, content generation, and creative AI tools.

Concepts Learned:

Autoencoders and model interpretability
Variational Autoencoders (VAEs)
Generative Adversarial Networks (GANs)
Transformer models for NLP and Vision
Tools and frameworks for GenAI experimentation

SKILLS GAINED

……………………..

Advanced Generative AI – Image Generation

Explore advanced image generation capabilities in GenAI. Master stable diffusion, denoising, embedding systems, and contrastive learning to create high-quality and context-aware visuals.

Concepts Learned:

Stable Diffusion techniques
Denoising methods for clarity and coherence
Shared embedding systems for image representation
Contrastive learning for model performance
Autoencoders in generative image workflows

SKILLS GAINED

……………………..

Generative AI Governance

Understand the ethical, regulatory, and governance aspects of Generative AI. Learn how to integrate governance frameworks, manage risks, and ensure compliance in AI projects.

Concepts Learned:

Ethical frameworks and AI principles
Governance structures and best practices
Risk management in AI projects
Lifecycle governance for data and models
Future trends in AI regulation and compliance

SKILLS GAINED

……………………..

Building a Data Science Portfolio

These projects are designed to help you:

Apply real-world skills in Python, data analysis, machine learning, and visualization
Solve practical business problems using data-driven approaches
Demonstrate your abilities to potential employers through a portfolio of completed work
Gain confidence in handling data projects independently

SKILLS GAINED

……………………..

SKILLS GAINED

……………………..

SKILLS GAINED

……………………..

SKILLS GAINED

……………………..

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