Masters Program – Machine Learning and Data Mining


Masters Program - Machine Learning and Data Mining

Becoming an Artificial Intelligence Engineer puts you on the path to an exciting, evolving career that is predicted to grow sharply into 2025 and beyond. Artificial intelligence and Machine Learning will impact all segments of daily life by 2025, with applications in a wide range of industries such as healthcare, transportation, insurance, transport and logistics and even customer service. The need for AI specialists exists in just about every field as companies seek to give computers the ability to think, learn and adapt.

Target Audience

This course is ideal for individuals having interest in Artificial Intelligence, Machine Learning or Text Mining. In other words, this course is meaningful for Python Developers, Robotics Engineer, and Fresh Graduates.


As such, there are no technical prerequisites for artificial intelligence. However, knowledge of python and mathematical aptitude will be beneficial.

Course Objectives

By the end of this Artificial Intelligence Course, you will be able to do the following: Design and build your own intelligent agents and apply them to create practical AI projects including  games, machine learning models, logic constraint satisfaction problems, knowledge-base systems, probabilistic models, agent decision-making functions and moreUnderstand and master the concepts and principles of  machine learning, including its mathematical and heuristic aspectsUnderstand neural networks and multi-layer data abstraction, empowering you to analyze and utilize data like never beforeImplement classical Artificial Intelligence techniques, such as search algorithms, minimax algorithm, neural networks, tracking, robot localizationAbility to apply Artificial Intelligence techniques for problem-solving and explain the limitations of current Artificial Intelligence techniquesFormalise a given problem in the language/framework of different AI methods (e.g., as a search problem, as a constraint satisfaction problem, as a planning problem, etc)  

Course Curriculum

Section 1 - Introduction to R/Python/ MATLAB
Python Basics, installation, variables and arithmeticPython data structures, lists, dictionaries, arraysFunctions and packagesIntroduction to Numpy
Section 2 - Linear Regression
Simple Linear Equation, formula, error estimationMaking predictions, Model performance (RMSE, MAPE)Multiple linear regressionGradient descentLinear Regression with PythonOverfitting, Underfitting, regularized linear regression
Section 3 - Logistic Regression
Introduction, Maximum Likelihood Function, Regression ModelLogistic Regression using Gradient Descent, making predictionsModel performance – confusion matrix, precision, recall, accuracyLogistic Regression with Python
Section 4 - Neural Networks
Introduction – Perceptrons, The XOR problemForward and backward propagationNN with Python
Section 5 - Support Vector Machines
Large margin classifiersKernelsSVM example and programming with python
Section 6 - Unsupervised Learning
Introduction with exampleIntroduction to K-means clustering
Section 7 - Cluster Analysis
Clustering introduction – Requirements, how to measure clustering performance, Dissimilarity measures K-means clustering details – Optimization objective, random initialization, Choosing k, Pros and cons of K-means Programming K-means clustering using PythonK-mediod clusteringHierarchical clustering, Agglomerative clusteringDensity based clustering – DBSCAN, OPTICS
Section 8 - Anomaly Detection
IntroductionMultivariate Gaussian distributionAnomaly Detection using Multivariate Gaussian distribution
Section 9 - Recommendation Systems
Problem formulationCollaborative filteringLow rank Matrix factorization
Section 10 - Text Mining Techniques
Text representation, vector space modelTF-IDF, bag of wordsTopic mining introductionSentiment analysis introduction
Section 11 - Machine learning with Python
One stop shop for details to python libraries such as Pandas, Numpy, scikit-learn.