Masters Program – Machine Learning and Data Mining

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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.

Prerequisites

  • 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 more
  • Understand and master the concepts and principles of  machine learning, including its mathematical and heuristic aspects
  • Understand neural networks and multi-layer data abstraction, empowering you to analyze and utilize data like never before
  • Implement classical Artificial Intelligence techniques, such as search algorithms, minimax algorithm, neural networks, tracking, robot localization
  • Ability to apply Artificial Intelligence techniques for problem-solving and explain the limitations of current Artificial Intelligence techniques
  • Formalise 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 arithmetic
  • Python data structures, lists, dictionaries, arrays
  • Functions and packages
  • Introduction to Numpy


Section 2 - Linear Regression

  • Simple Linear Equation, formula, error estimation
  • Making predictions, Model performance (RMSE, MAPE)
  • Multiple linear regression
  • Gradient descent
  • Linear Regression with Python
  • Overfitting, Underfitting, regularized linear regression


Section 3 - Logistic Regression

  • Introduction, Maximum Likelihood Function, Regression Model
  • Logistic Regression using Gradient Descent, making predictions
  • Model performance – confusion matrix, precision, recall, accuracy
  • Logistic Regression with Python


Section 4 - Neural Networks

  • Introduction – Perceptrons, The XOR problem
  • Forward and backward propagation
  • NN with Python


Section 5 - Support Vector Machines

  • Large margin classifiers
  • Kernels
  • SVM example and programming with python


Section 6 - Unsupervised Learning

  • Introduction with example
  • Introduction 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 Python
  • K-mediod clustering
  • Hierarchical clustering, Agglomerative clustering
  • Density based clustering – DBSCAN, OPTICS


Section 8 - Anomaly Detection

  • Introduction
  • Multivariate Gaussian distribution
  • Anomaly Detection using Multivariate Gaussian distribution


Section 9 - Recommendation Systems

  • Problem formulation
  • Collaborative filtering
  • Low rank Matrix factorization


Section 10 - Text Mining Techniques

  • Text representation, vector space model
  • TF-IDF, bag of words
  • Topic mining introduction
  • Sentiment analysis introduction


Section 11 - Machine learning with Python

  • One stop shop for details to python libraries such as Pandas, Numpy, scikit-learn.