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Quick Facts

Medium Of InstructionsMode Of LearningMode Of DeliveryFrequency Of Classes
EnglishSelf Study, Virtual ClassroomVideo and Text BasedWeekends

Courses and Certificate Fees

Certificate AvailabilityCertificate Providing Authority
yesMIT Cambridge

The Syllabus

Module 1: Introduction to the AI Landscape
  • Understanding the data: What is it telling us?
  • Prediction: What is going to happen?
  • Decision Making: What should we do?
  • Causal Inference: Did it work?

Module 2: Data Exploration - Structured Data
  • Asking the right questions to understand the data.
  • Understanding how data visualization makes data clearer.
  • Performing Exploratory Data Analysis using PCA.
  • Clustering the data through K-means & DBSCAN clustering.
  • Evaluating the quality of clusters obtained.

Module 3: Prediction Methods - Regression
  • The idea of regression and predicting a continuous output.
  • How do you build a model that best fits your data?
  • How do you quantify the degree of uncertainty?
  • What do you do when you don’t have enough data?
  • What lies beyond linear regression?

Module 4: Decision Systems
  • Understand the Decision Tree model and the mechanics behind its predictions.
  • Learn to evaluate the performance of classification models.
  • Understand the concepts of Ensemble Learning and Bagging.
  • Learn how Random Forests aggregate the predictions of multiple Decision Trees.

Module 5: Data Exploration - Unstructured Data
  • Understand the concept of unstructured data, and how natural language is an example.
  • Understand the business applications for Natural Language Processing.
  • Learn the techniques and methods to analyze text data.
  • Apply the knowledge gained towards the business use case of sentiment analysis.

Module 6: Recommendation Systems
  • Learn the concept of recommendation systems and potential business applications.
  • Understand the sparse data problem that necessitates recommendation systems.
  • Learn about potentially simple solutions to the recommendation problem.
  • Understand the ideas behind Collaborative Filtering Recommendation Systems.

Module 7: Data Exploration - Temporal Data
  • Understand temporal data and how it represents a different data modality.
  • Understand the idea behind Time Series forecasting
  • Learn about the concept of Stationary Time Series, testing for stationarity and conversion techniques to transform non-stationary time series into stationary.

Module 8: Prediction Methods - Neural Networks
  • Understand the key concepts involved in Neural Networks.
  • Learn about the encoding process taking place in the neural network layers, and how non-linearities are introduced.
  • Understand how the forward propagation happens through the layered architecture of neural networks and how the first prediction is achieved.
  • Learn about the cost function used to evaluate the neural network’s performance, and how gradient descent is used in a backpropagation cycle to minimize error.
  • Understand the critical optimization techniques used in gradient descent

Module 9: Computer Vision Methods
  • Learn about spatial concepts of images such as locality and translation invariance.
  • Understand the working of filters and convolutions, and how they achieve feature extraction to generate encodings.
  • Learn about how these concepts are used in the structure of Convolutional Neural Networks (CNNs) and understand what CNNs actually learn from image data.

Module 10: Workflows and Deployment



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