AI & Machine Learning Professional
Master foundational and advanced deep learning skills used to build intelligent systems — Our comprehensive curriculum provides a hands-on approach to deep learning. You'll delve into foundational concepts, explore state-of-the-art techniques, and gain practical experience through real-world projects and assignments.
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AI & ML Professional
AI and machine learning is transforming how organisations operate — enabling smarter decision-making, automation, prediction, and innovation. Though demand for AI skills is growing fast, many teams struggle to find professionals who can combine technical understanding with practical application.
This course bridges the gap by teaching you the foundations of machine learning and the modern techniques used in real AI projects — with a strong focus on real-world cases and hands-on learning.
Career roles after completion
Machine Learning Engineer
AI Developer
Deep Learning Practitioner
AI Consultant
ABOUT THE COURSE
AI & Machine Learning Curriculum
Module 1 — Foundational Concepts
This module introduces the core principles of Machine Learning and Deep Learning, covering supervised, unsupervised, and reinforcement learning, as well as key concepts such as features, labels, models, training, and testing. Participants will also gain hands-on experience with essential tools like NumPy and Pandas. Through practical projects in e-commerce and banking, learners will build and evaluate regression and classification models, working with real-world cases such as laptop price prediction, diamond price estimation, fraud detection, and customer churn analysis. The module combines theory, applied assignments, and structured feedback to build a strong foundation in machine learning. Module 1: Part 1: ML & Deep Learning basics + supervised/unsupervised/RL, model selection, core concepts, NumPy & Pandas Part 2: Regression project (Laptop price prediction) — preprocessing + exploration Part 3: Regression — training, tuning, using the model + assignment (Diamond price prediction) + review Part 4: Classification project (Fraud detection) — preprocessing + exploration + training Part 5: Classification — tuning + using the model + assignment (Customer churn prediction) + review
Module 3 - Capstone
This module focuses on building an Autonomous Self Driving Car using Simulator.
Module 2 - Neural Networks and Deep Learning (Computer Vision Direction)
In this module, learners move from traditional machine learning into deep learning for computer vision. You will build and improve convolutional neural network (CNN) models using TensorFlow or PyTorch, working with real-world projects in the automotive and life-science domains. The module also introduces how to deploy and serve deep learning models in the cloud using Google Cloud Platform or AWS, with hands-on assignments and structured feedback throughout. Module 2: Part 1: Deep learning for computer vision + project setup (driver distraction detection), TensorFlow or PyTorch, data preprocessing and exploration Part 2: CNN concepts, training the model, using pre-trained CNNs Part 3: Improving the model with transfer learning + assignment (plant disease classification) + review and feedback Part 4: Deploying and serving the computer vision model to the cloud (GCP or AWS) + assignment + review and feedback
THIS INDUSTRY
The Skills Powering Modern Business
AI and machine learning are now core technologies across industries like finance, healthcare, manufacturing, retail, and cybersecurity. Organisations use machine learning to automate decisions, detect anomalies, improve customer experiences, and build smarter products.
As AI adoption grows, demand is rising for professionals who understand how machine learning works in practice — from data and training to evaluation and deployment. This makes AI & machine learning one of the fastest-growing and most in-demand areas in tech today.
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Get to Know Your Teacher
David Oyediran
David is an AI Engineer Consultant at Capgemini Engineering with over eight years of experience designing intelligent solutions that create real value for both businesses and society. His work spans healthcare, automotive, marine, energy, and sustainability, and he has contributed to projects including voice-controlled robotics for human-robot interaction (Volvo Cars), natural language processing systems, and Reinforcement Learning and computer vision models for autonomous vehicles.
David enjoys bridging cutting-edge technology with real human impact, and that passion extends naturally into teaching. He finds great fulfillment in mentoring learners and supporting students as they build confidence and strengthen their skills.
Outside of work, you’ll often find David on a football pitch—either playing or cheering from the sidelines. David looks forward to guiding your learning journey and exploring the possibilities of AI with you.
8+ Years
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