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Master Certificate in AI and Machine Learning

Build intelligent systems using AI, ML, Deep Learning, and Generative AI. Master Python, data science, and key ML techniques such as supervised learning and ensemble models. Work with TensorFlow, Keras, PyTorch, and large language models (LLMs). Deploy real-world AI projects including image, video, and text generation use cases

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Start Date

14th Febuary 2025

Program Duration

6 months

Mon & Wed, 6-9 pm

Learning Format

Intructor-Led (Live Project-Based Learning)

Beginners who want structure & accountability

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

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

$6000

Mentor-Guided (Self-Paced + Projects)

Motivated learners who want flexible learning with support

Flexible + weekly mentor
check-in

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

$6000

Intructor-Led (Live Project-Based Learning)

Independent learners exploring a career path

Start Anytime

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

$499

Intructor-Led (Live Project-Based Learning)

Beginners who want structure & accountability

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

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

$6000

Mentor-Guided (Self-Paced + Projects)

Motivated learners who want flexible learning with support

Flexible + weekly mentor check-in

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

$6000

Intructor-Led (Live Project-Based Learning)

Independent learners exploring a career path

Start Anytime

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

$499

Program Overview

Master AI, Machine Learning, and Generative AI through hands-on training in Python, deep learning, and agentic frameworks. Gain practical, real-world experience by working on projects that develop high-demand skills such as AI automation, large language model (LLM) proficiency, and advanced machine learning techniques. Equip yourself to lead in the AI-driven future and advance your career with this comprehensive program.

Course Outline

Our curated Master Certificate in AI and Machine Learning program is designed to propel you toward a successful career in artificial intelligence and machine learning. Explore cutting-edge advancements such as ChatGPT, Generative AI (GenAI), and large language models (LLMs). Gain hands-on experience creating conversational chatbots, mastering content generation, and leveraging the latest AI technologies to solve real-world problems.

Download Outline

Python Fundamentals

Develop essential Python programming skills that form the foundation for AI and ML. Learn to implement algorithms, manage data, and build intelligent systems with Python.

Concepts Learned:

Fundamentals of programming with Python
Data types, operators, and string functions
Loops, conditionals, and variable scope
Functions, methods, attributes, and access modifiers
Multi-threading for performance optimization
Setting up Python IDEs and Jupyter Notebook environments

SKILLS GAINED

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SKILLS GAINED

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Python Fundamentals

Develop essential Python programming skills that form the foundation for AI and ML. Learn to implement algorithms, manage data, and build intelligent systems with Python.

Concepts Learned:

Fundamentals of programming with Python
Data types, operators, and string functions
Loops, conditionals, and variable scope
Functions, methods, attributes, and access modifiers
Multi-threading for performance optimization
Setting up Python IDEs and Jupyter Notebook environments

SKILLS GAINED

……………………..

Python Fundamentals

Develop essential Python programming skills that form the foundation for AI and ML. Learn to implement algorithms, manage data, and build intelligent systems with Python.

Concepts Learned:

Fundamentals of programming with Python
Data types, operators, and string functions
Loops, conditionals, and variable scope
Functions, methods, attributes, and access modifiers
Multi-threading for performance optimization
Setting up Python IDEs and Jupyter Notebook environments

SKILLS GAINED

……………………..

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)
NumPy and linear algebra applications
Probability and statistical methods (Z-test, T-test, ANOVA)
Data manipulation and wrangling with Pandas
Data visualization using Matplotlib, Seaborn, Plotly, and Bokeh
End-to-end statistical analysis pipelines

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)
NumPy and linear algebra applications
Probability and statistical methods (Z-test, T-test, ANOVA)
Data manipulation and wrangling with Pandas
Data visualization using Matplotlib, Seaborn, Plotly, and Bokeh
End-to-end statistical analysis pipelines

SKILLS GAINED

……………………..

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

SKILLS GAINED

……………………..

Machine 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
Unsupervised learning: clustering and anomaly detection
Ensemble learning: bagging, boosting, stacking
Overfitting, underfitting, and model evaluation
ML pipeline and MLOps concepts
Recommendation engine design
Comparing frameworks: TensorFlow, Keras, PyTorch

SKILLS GAINED

……………………..

Machine 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
Unsupervised learning: clustering and anomaly detection
Ensemble learning: bagging, boosting, stacking
Overfitting, underfitting, and model evaluation
ML pipeline and MLOps concepts
Recommendation engine design
Comparing frameworks: TensorFlow, Keras, PyTorch

SKILLS GAINED

……………………..

Machine 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
Unsupervised learning: clustering and anomaly detection
Ensemble learning: bagging, boosting, stacking
Overfitting, underfitting, and model evaluation
ML pipeline and MLOps concepts
Recommendation engine design
Comparing frameworks: TensorFlow, Keras, PyTorch

SKILLS GAINED

……………………..

Deep Learning Specialization

Master the fundamentals of deep learning, from neural networks to advanced architectures like CNNs, RNNs, and transformers. Learn optimization techniques and practical applications in NLP and computer vision.

Concepts Learned:

Artificial and deep neural networks
Forward and backward propagation in DNNs
Hyperparameter tuning and performance optimization
Convolutional neural networks (CNNs) for vision tasks
Recurrent neural networks (RNNs) for sequential data
Transfer learning and object detection
Autoencoders and model interpretability
Transformer models for NLP

SKILLS GAINED

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Deep Learning Specialization

Concepts Learned:

Artificial and deep neural networks
Forward and backward propagation in DNNs
Hyperparameter tuning and performance optimization
Convolutional neural networks (CNNs) for vision tasks
Recurrent neural networks (RNNs) for sequential data
Transfer learning and object detection
Autoencoders and model interpretability
Transformer models for NLP

SKILLS GAINED

……………………..

Deep Learning Specialization

Concepts Learned:

Artificial and deep neural networks
Forward and backward propagation in DNNs
Hyperparameter tuning and performance optimization
Convolutional neural networks (CNNs) for vision tasks
Recurrent neural networks (RNNs) for sequential data
Transfer learning and object detection
Autoencoders and model interpretability
Transformer models for NLP

SKILLS GAINED

……………………..

Generative AI Literacy

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:

Transformer algorithms and neural networks
Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)
Large Language Models (LLMs) including ChatGPT
AI-driven image and video generation
Tools and frameworks for GenAI experimentation

SKILLS GAINED

……………………..

Generative AI Literacy

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:

Transformer algorithms and neural networks
Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)
Large Language Models (LLMs) including ChatGPT
AI-driven image and video generation
Tools and frameworks for GenAI experimentation

SKILLS GAINED

……………………..

Generative AI Literacy

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:

Transformer algorithms and neural networks
Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)
Large Language Models (LLMs) including ChatGPT
AI-driven image and video generation
Tools and frameworks for GenAI experimentation

SKILLS GAINED

……………………..

Generative AI Essentials

Explore the fundamentals, evolution, and applications of Generative AI across multiple domains. Gain practical experience with industry-leading tools like GPT, DALL·E, Stable Diffusion, and Synthesia.

Concepts Learned:

Generative AI vs. discriminative AI
Text, image, code, audio, and video generation
ChatGPT and conversational AI applications
Implementing models with Hugging Face, TensorFlow, and PyTorch
Industry-wide applications of GenAI

SKILLS GAINED

……………………..

Generative AI Essentials

Concepts Learned:

Generative AI vs. discriminative AI
Text, image, code, audio, and video generation
ChatGPT and conversational AI applications
Implementing models with Hugging Face, TensorFlow, and PyTorch
Industry-wide applications of GenAI

SKILLS GAINED

……………………..

Generative AI Essentials

Concepts Learned:

Generative AI vs. discriminative AI
Text, image, code, audio, and video generation
ChatGPT and conversational AI applications
Implementing models with Hugging Face, TensorFlow, and PyTorch
Industry-wide applications of GenAI

SKILLS GAINED

……………………..

Foundation Models & GenAI Platforms

Learn how foundation models and pre-trained platforms power the next wave of AI. Explore IBM Watsonx, Hugging Face, Granite, GPT, and FLAN models for enterprise and open-source use cases.

Concepts Learned:

Deep learning and LLM fundamentals
Foundation models and pre-trained architectures
Text-to-text, text-to-image, and text-to-code generation
IBM Watsonx for enterprise AI applications
Hugging Face as an open-source hub for GenAI

SKILLS GAINED

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Foundation Models & GenAI Platforms

Learn how foundation models and pre-trained platforms power the next wave of AI. Explore IBM Watsonx, Hugging Face, Granite, GPT, and FLAN models for enterprise and open-source use cases.

Concepts Learned:

Deep learning and LLM fundamentals
Foundation models and pre-trained architectures
Text-to-text, text-to-image, and text-to-code generation
IBM Watsonx for enterprise AI applications
Hugging Face as an open-source hub for GenAI

SKILLS GAINED

……………………..

Foundation Models & GenAI Platforms

Learn how foundation models and pre-trained platforms power the next wave of AI. Explore IBM Watsonx, Hugging Face, Granite, GPT, and FLAN models for enterprise and open-source use cases.

Concepts Learned:

Deep learning and LLM fundamentals
Foundation models and pre-trained architectures
Text-to-text, text-to-image, and text-to-code generation
IBM Watsonx for enterprise AI applications
Hugging Face as an open-source hub for GenAI

SKILLS GAINED

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SKILLS GAINED

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SKILLS GAINED

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SKILLS GAINED

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SKILLS GAINED

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SKILLS GAINED

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SKILLS GAINED

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SKILLS GAINED

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SKILLS GAINED

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Applied Data Science with Python

Machine Learning

Deep Learning Specialization

Generative AI Literacy

Generative AI Essentials

Foundation Models & GenAI Platforms

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