LEARNING OUTCOMES

• Learn basic concepts of Python
• Acquire rudimentary skills to write programs in Python
• Ability to use Python for Data Science & Machine learning
• Get application-ready with essential Python libraries & tools

Content Covered

• Basic Python Programming
  • Variable and data types
  • Conditional statements
  • Loops
  • Functions
• Essential Python libraries for data science
  • Pandas
  • Numpy
  • Scikit
• Setting up Python for Machine Learning

LEARNING OUTCOMES

• Master the mathematical foundation required for writing programs
• Learn mathematical and statistical foundations required for AI & ML
• Acquire mathematical knowledge to build algorithms for data analysing
• Apply statistical analysis techniques using essential softwares on data sets

Content Covered

• Linear Algebra
• Statistics
• Probability Theory
• Statistical Tools (CSV, Excel)

LEARNING OUTCOMES

• Acquire practical skills in data analyzing, handling & visualization using Python tools
• Perform mathematical operations on a wide range of data using NumPy
• Operate Pandas to sort through and rearrange data, run analyses, and build data frames
• Ability to analyze by visualizing data with Matplotlib

Content Covered

• Python Programming for AI & ML
  • Essential Python libraries for data analysis
  • Data storage and manipulation by NumPy
  • Data Visualization using Matplotlib
  • Data Analysis with Pandas
  • Basic introduction to Sci-kit-learn

LEARNING OUTCOMES

• Understand Artificial Intelligence and Machine Learning fundamentals
• Demonstrate a comprehensive knowledge of the nature of the data and techniques used for pre-processing the data for machine  learning
• Introduction to major machine learning algorithms like Classifiers (for image, spam, fraud), Regression (stock price, housing price, etc.), Clustering (unsupervised classifiers)
• Demonstrate a deep critical understanding of algorithms and mathematics behind established ML approaches

Content Covered

• Introduction to Machine Learning & AI
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Machine Learning Algorithms (Regression, Classifiers, Clustering)
• Machine Learning Task (dataset, data cleaning, algorithm selection, training & testing model)

LEARNING OUTCOMES

• Understand the Basic python tools used for Computer Vision
• Understand image processing python packages to enable them to write scripts for text pre-processing
• Learn popular machine learning algorithms, Feature Selection, and Mathematical intuition behind it
• Understand basic concepts and standard tools used in computer vision

Content Covered

• Core Python for computer vision
  • Strings
  • Regex
• Machine Learning algorithms
  • Regression
  • KNN
  • SVM
• Computer vision tools
  • Keras
  • TensorFlow

LEARNING OUTCOMES

• Concepts of deep learning to build artificial neural networks and traverse layers of data abstraction and get a solid understanding of deep learning
• Develop and build fully automated CV algorithms USING YOLO
• Develop the usage of Deep learning models like CNN and RNN
• Gain insights about advancements in CV, AI, and Machine Learning techniques

Content Covered

• Introduction to Computer Vision (CV)
• Deep Learning Network Models
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Introduction to Keras Model Life-Cycle
• Image Data Manipulation using Pillow Python library.
• Convert Images to NumPy Arrays and Back

The purpose of this module is to discuss and explain the role of Artificial Intelligence and Machine Learning in an organization and their influence on its overall performance and competence. Learners will be encouraged to pick a research/development project that displays their past learning in the AI & ML domain. It is meant to understand various aspects of AI, such as Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision, to name a few. It also endeavors to highlight the role and significance of AI & ML during the planning, decision-making, and implementation of change in the organization.

Upon completing the module, the participants will have comprehensive knowledge and the ability to demonstrate their expertise in Artificial Intelligence and Machine Learning to potential employers or educational programs.

Content Covered

• Clarifying the terms of the research
• Suggesting areas of reading
• Apply the knowledge base and abilities taught throughout the course to a real-world scenario
• The Problem, Understanding It, and Getting Data
• Frame a business issue in a manner that can be solved with AI & ML
• Apply Exploratory Data Analysis and Modeling
• Identify the methodology or algorithm that will handle the proposed challenge
• Reviewing the proposed methodology
• Establishing a research timetable, including initial dates for further meetings between the student and supervisor
• Advising students about appropriate standards & conventions concerning the assessment.
• Providing means of contact in addition to tutorials
• Educate learners to research and write their results and thoughts correctly, clearly, logically, and to a high-professional degree