Masters in AI & Robotics

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Course Description

Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors. In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system. You will learn about training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning. The program is a blend of data science, deep learning, business analytics and visualization with the application of advanced analytics models for artificial intelligence, deep learning and cognitive computing. Designed to give students a comprehensive analytics education with projects and seminars by industry leaders and hands-on learning.

What you’ll learn

  • The basics of machine learning
  • How to perform cross-validation to avoid overtraining
  • Several popular machine learning algorithms
  • How to build a recommendation system
  • What is regularization and why it is useful

Basic Eligibility

Graduate in Engineering in IT / Computer Science / Electronics / IT & Telecommunications / Electrical / Instrumentation and other computer associated streams. No year down & no paper down from the Candidates to get placements. 60 % marks as aggregate in qualifying examination. OR Post Graduate Degree in Engineering Sciences with corresponding basic degree (e.g. MSc in Computer Science, IT, Electronics) with 60% marks as aggregate in qualifying examination. Also Equally Required:

  • Strong liking for basic Mathematics, Logical Reasoning
  • Analytical Bent of Mind
  • Desire to Learn
  • Willing to put sincere efforts and time to pick up new concepts

Job Opportunities

  • Data Scientist & Machine Learning Engineer
  • Design Engineer
  • Data Engineer
  • Data Scientist
  • Data Science Specialist
  • Machine Learning Engineer
  • Senior Data Scientist
  • Data scientist visualization

Introduction

  • What is Data Science
    • What is Data Science composed of?
    • Fields of Study in Data Science, and Relationships between them
  • Role of Machine Learning in Data Science
  • Data: Sources, Exploration, Modeling, Visualization

Foundation

  • Brief introduction to Octave
    Mathematics
    • Scalars, Vectors, Matrices
    • Matrix Operations, Matrix Relationships
    • Eigenvalues, Eigenvectors
    • Dimensionality Reduction (PCA)
    • Basic Calculus
    • Limits
    • Derivatives
    • Statistics
    • Mean, Mode, Median
    • Variance, Standard Deviation
      – Probability
      – Events, Sample Space, Random Variables
      – Mutually Exclusive Events
      – Venn Diagrams
      – Bayes Theorem
      – Inference (Introduction)
      – Sampling, Hypothesis Testing

Python Programming

    • Hands on with Python

– Getting Started with Jupyter Notebook

  • Programming Semantics

– Control Structures, Loops

    • – List, Tuple, Dict, Set
    • – List and Dict Comprehension
      • Functions and Modules

    – Function as objects

    • – Standared Library, pip
      • File/Data Handling, Exceptions
      • Functional Programming
      • Object Oriented Programming
      • Python Programming Mini Project
      • Python Libraries for Machine Learning

    – Numpy

    • – Array Indexing, Operations
  •  
  • – Example Application: Hands on with Real world Dataset
      – Scalars, Arrays

Machine Learning

      • Regression

    – Simple Linear Regression

    • – Multiple Linear Regression
    • – Decision Tree Regression
    • – Example Application: Hands on with Real world Dataset
      • Classification

    – Logistic Regression

    • – K-Nearest Neighbour (KNN)
    • – Support Vector Machine (SVM)
    • – Naive Bayes
    • – Evaluating Classification Model Performance
    • – Example Application: Hands on with Real world Dataset
      • Clustering

    – K-Means Clustering

    • – Example Application: Hands on with Real world Dataset
      • Artificial Neural Networks (ANN)
      • Applying Machine Learning: Deployment

    – Python Requests

    • – Python Flask
    – Deployment with Cloud Services