Artificial Intelligence and Machine Learning

Curriculum

Best-in-class content by leading faculty and industry leaders in the form of videos, cases studies, projects, assignments and live sessions.

• Artificial Intelligence & Machine Learning Introduction (7 Hours.)

  This module introduces the learner with the landscape of AI and Machine Learning, gives a crystal clear understanding of how multiple technologies like Artificial Intelligence, Machine Learning, Deep Learning, NLP, Data Science are related to each other. Moreover this module also gives a brief introduction to Deep Learning to the learners.

  • AI Landscape
  • What is Machine Learning?
  • Machine Learning Landscape
  • Deep Learning Overview
  • Activity and Quiz
• Python A to Z (29 Hours.)

  The core takeaway of this module is the skillset of Python & its packages for Data Analytics. The learner gets to learn python from very basics, going through data types, control flow and functions the learner will get to work on data files, will learn how to import, export, clean, manipulate, visualize, analyse data to generate insights from data to answer business questions. This completion of this module makes the learner an entry level Data Analyst and a Python Programmer.

  • Python Basics - Data Types, Control Flow and Functions
  • Python - OOPs and common packages
  • Numpy and Pandas for Data Science
  • Data Visualization with Python
  • Statistics with Data
  • Exploratory Data Analytics with Python
  • Quiz
• Github (1 Day / 2 Hours)
  • Git
  • Github/Gitlab
  • Collaborators
  • Git Basic Commands
  • Branch, Merge & Rebase
• Machine Learning- Fundamentals (20 Hours.)

  The objective of this module is to make learner get started with using Machine Learning algorithms, learn and identify different performance metrics to assess quality of ML algorithms. This module takes the learner through all ML algorithms which are widely used in Industry. At the end learner also goes through Ensemble Learning models like Random Forest, Gradient Boosting Trees and XGBoost.

  • Getting started with Scikit-learn
  • Linear Regression
  • Logistic Regression
  • Decision Tree & KNN
  • Project
  • Ensemble Learning
• Machine Learning-Advanced (9 Days)

  Learning objectives of this module includes pattern discovery using unsupervised machine learning problems like clustering, anomaly detection and dimensionality reduction. The module also takes the learner through more powerful and highly non linear algorithms like SVM and ANN. The end of this module makes the learner eligible to apply for Entry level Data Scientist jobs.

  • Unsupervised Machine Learning 6
  • Support Vector Machine 2
  • Artificial Neural Networks 10
  • Different Strategies
• Deep Learning and Computer Vision (29 Hours.)

  The learning objectives of this module includes getting started with Deep Learning algorithms, learning and understanding basics of image processing with openCV. The modules introduces the most popular framework used in industry for deep learning and computer vision - TensorFlow to the learners. The modules makes learner work with the most demanding use cases of computer vision in industry like single label and multilabel image classification, using transfer learning for building powerful image classifiers and taggers and working with object localization and object detection using TensorFlow. The end of this module makes the learner an entry level computer vision engineer.

  • Image Processing with openCV
  • Neural Networks overview
  • Getting started with Tensorflow & Tensorboard
  • Convolutional Neural Networks
  • Transfer Learning with CNN for Computer Vision
  • Object Localization and Object Detection
• Deploying AI apps and AI with Cloud (16 Hours.)

  This module takes the learner through the approaches of deploying an ML model in production, production common challenges and best practices. The module also introduces the best practices at each phase of an ML lifecycle - data cleaning, feature engineering, overfitting, underfitting, speed improvement and deployment. This module also takes the learner through available cloud services for working with ML projects, pretrained APIs from different cloud service providers and more exclusively through Microsoft Azure services for AI and ML. The end of this module makes the learner an AI Architect and an AI Engineer.

  • Deploying apps with Flask
  • AutoML and Best practices in ML and Deep Learning
  • ML with Azure Cloud