Our MACHINE LEARNING program provides students with a broad knowledge in two main areas of Machine Learning: supervised and unsupervised. The program introduces to systems that learn from experience and outline the problems based on classification, clustering and regression.

It will cover topics Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines, Ensemble Learning with Bagging and Boosting, Random Forest , k-NN, Dimensionality Reduction,  Principal Component Analysis, K-means algorithm, Self-Organizing Feature Maps, Apriori algorithm, FP-growth algorithm, Dimensionality Reduction using Principal Component Analysis, Anomaly Detection and Semisupervised Learning.

The program will primarily use the Python programming language and assumes familiarity with linear algebra, probability theory, and programming in Python.

KNOWLEDGE & SKILLS GAINED

Machine Learning students will understand the foundations of Linear Regression, Linear and Logistic regression, Support Vector Machines and other models in machine learning. The course will expose to the fundamentals concepts of regression and classification with practical implementation with different Python packages like Numpy, Scipy and Scikit-Learn.

Student will be able to build models for prediction using machine learning concepts, use classification methods for building prediction models, apply learning algorithms to improve predictive performance, outline the required instances using representation learning, build an anomaly detection system by separating outliers, apply clustering algorithms for observations based on similarity and construct predictor’s models using machine learning techniques.

The project work will build your technical skills by providing a methodological approach towards problem-solving using models in Machine Learning.