Artificial Intelligence & Machine Learning
Build practical AI and Machine Learning skills through structured learning, hands-on implementation and a real-world capstone project.
Course Curriculum
A practical learning journey designed to build your understanding of Artificial Intelligence and Machine Learning — from core concepts and Python to model development, neural networks and real-world applications.
Modules
Understand the relationship between Artificial Intelligence, Machine Learning and Deep Learning, along with real-world use cases across different industries.
Understand the relationship between Artificial Intelligence, Machine Learning and Deep Learning, along with real-world use cases across healthcare, finance, retail and manufacturing.
Explore supervised, unsupervised and reinforcement learning approaches and understand which technique suits different categories of business problems.
Build the Python foundation required for working with machine learning datasets, data preparation and exploratory analysis.
Use NumPy arrays and Pandas DataFrames to load, clean and manipulate structured datasets in preparation for model building.
Create exploratory visualizations using Matplotlib and Seaborn to identify trends, distributions and relationships within data.
Learn how supervised machine learning algorithms can be used to predict continuous and categorical outcomes and solve classification problems.
Build linear and logistic regression models to predict continuous and categorical outcomes while understanding coefficients, residuals and model fit.
Implement Decision Trees, Random Forests and Support Vector Machines to classify data and compare model performance.
Explore techniques for discovering hidden patterns in unlabeled data and learn how to evaluate machine learning models effectively.
Apply K-Means and hierarchical clustering to group unlabeled data and identify hidden patterns and customer segments.
Use accuracy, precision, recall, F1-score and cross-validation to assess model performance and avoid overfitting.
Understand the fundamentals behind neural networks and gain hands-on experience building a simple neural network with TensorFlow and Keras.
Understand perceptrons, activation functions and how forward and backward propagation drive learning in neural networks.
Build and train a simple neural network using TensorFlow/Keras to solve a classification problem.
Bring the complete machine learning workflow together, from feature engineering and model improvement to deployment and a real-world capstone project.
Learn techniques to improve model performance through feature selection, scaling and an introduction to deploying models as APIs.
Apply the complete ML workflow — from data collection to model deployment — on a real-world dataset and present your findings.
What You'll Learn
Build practical Artificial Intelligence and Machine Learning skills from core concepts to real-world model development.
AI & Machine Learning Fundamentals
Understand Artificial Intelligence, Machine Learning and the different approaches used to build intelligent systems.
Python for Machine Learning
Work with Python and essential libraries such as NumPy and Pandas for data handling, analysis and visualization.
Supervised Learning
Build regression and classification models and understand their application to practical machine learning problems.
Unsupervised Learning
Explore clustering techniques, discover patterns in datasets and evaluate machine learning model performance.
Neural Networks & Deep Learning
Learn neural network fundamentals and work with TensorFlow and Keras to develop deep learning solutions.
Real-World AI/ML Application
Apply the complete AI/ML workflow through feature engineering, model development, deployment and a capstone project.
Ready to Build Your AI & Machine Learning Skills?
Take the next step toward practical AI/ML learning with hands-on projects, real-world applications and a complete capstone experience.
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