Program overview
This 2-Month Machine Learning & AI Program is designed to help learners understand how intelligent systems learn from data and how machine learning models are developed, evaluated, and applied to real-world problems.
The program begins with essential Python programming for data science, followed by working with libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn. Learners will understand how to collect, clean, preprocess, explore, and prepare datasets before building machine learning models.
The course covers important Supervised Learning algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forest, K-Nearest Neighbors, Naive Bayes, and Support Vector Machines. Learners will also explore Unsupervised Learning concepts including clustering techniques such as K-Means and dimensionality reduction fundamentals.
Students will learn how to perform train-test splitting, feature selection, feature scaling, model training, prediction, and model evaluation using metrics such as accuracy, precision, recall, F1-score, confusion matrix, and regression evaluation measures.
The Artificial Intelligence module introduces the fundamentals of AI, intelligent systems, Natural Language Processing, recommendation concepts, neural network fundamentals, and Generative AI awareness. Learners will understand where different AI and ML techniques are used in real-world applications.
The training follows a strongly hands-on, scenario-based, and project-oriented approach, using practical datasets, coding exercises, case studies, mini-projects, and a final Machine Learning application.
By the end of the program, learners will be able to prepare datasets, build machine learning models, evaluate their performance, interpret predictions, and develop practical AI-powered solutions using Python.