CAAI is an intensive application oriented, real-world scenario based program in Artificial Intelligence (AI). This is a intensive skill oriented, practical training program required for building Applications using various techniques. It is designed to give the participant enough exposure to the variety of applications that can be built using techniques covered under this program.



Tools Covered

This program strives to increase the learning of various tools, techniques and methods of various AI/ML techniques. The emphasis is on applying these techniques to the real-world systems and be able to build AI based systems.

Course Curriculum

This module covers the methods, methodologies and techniques of statistics and probability. These techniques are helpful to obtain supporting evidence, identify factors to construct models, uncover relationships and understand variation in the processes. Regression (glm) is used to predict an outcome.

Machine learning are set of powerful techniques to learn hidden patterns, predict and categorize objects based on various features with out being explicitly programmed. This will revolutionize the enterprise applications by its ability to learn and adopt to new circumstances without much human intervention.

  • Qualitative and Quantitative Data
  • Measures of Central Tendency, Positions, Dispersion, Distribution
  • Relationships: Covariance, Correlation Coefficient and Chi Square
  • Probability distributions (Continuous and Discrete)
  • Density Functions and Cumulative functions
  • Bayesian Methods
  • Hypothesis Testing 
  • Contingency tables
  • Chi-Square test and Fisher’s exact test)
  • t-test, z-test and F-test
  • One way ANOVA Fisher’s LSD, Tukey’s HSD).
  • Linear and Multi-variate regression
  • R-square and goodness of fit
  • Residual Analysis
  • Identifying significant features, feature reduction using AIC,
  • Non-normality,Heteroscedasticity and multi-collinearity
  • Regularization methods ( Lasso, Ridge and Elastic nets) 
  • Categorical Variables in Regression (Poisson)
  • ML Techniques overview
  • Bias & Variance 
  • Validation Techniques (Cross-Validations)
  • Dimensionality reduction – Principal components analysis 
  • Feature Engineering, Unbalanced data treatment
  • Distance measures
  • Different clustering methods (Distance, Density, Hierarchical)
  • K-Medoids, k-Mode and density-based clustering
  • Naive Bayes Classifier
  • Logistic Regression
  • K-Nearest Neighbors
  • Support Vector Machines
  • Linear Discriminant Analysis
  • Decision Trees (ID4, C4.5, CART)
  • Bagging & boosting
  • Random forest
  • Gradient Boosting Machines and XGBoost
  • Apriori, FP-growth, Eclat Algorithms
  • Recommender Systems

Artificial Intelligence is utilized heavily in computizing cognitive functions such as speech and Vision. Often these functions are achieved through the use of Neural Networks. In this module, we will study very popular NN architectures for achieving various cognitive functions such as Object recognition, natural language processing besides explore reinforcement learning.

These case studies needs tha application of a range of AI/ML techniques learned during the course. The Data-Hack type of approach to these Case studies provides a quick, intensive, competitive activity culminating application and intellectual experience for the participants. It is similar to a mini-project with an informal presentation of the results.

  • AI: Application areas, ANNs
  • Gradient Descent, Perceptron, MLP, FFN, Back Propagation
  • Regularization – Dropout and Batch normalization
  • ANNs for Structured Data
  • Conversational AI
  • RNNs (Text generation (Image Captioning)
  • Long Short-Term Memory
  • Auto Encoders
  • Multi-Agent Reinforcement Learning
  • Markov Process and Monte Carlo Methods
  • Deep Q-Learning

Understanding customers deeply will help customize service offerings and extending incentives. We will explore various aspects of Customer 360.Understanding customers deeply will help customize service offerings and extending incentives. We will explore various aspects of Customer 360.

In manufacturing, the finished products are physically inspected for quality issues. Automatic visual defect detection has the potential to reduce the cost of QA and ensuring 100% coverage.

  • We will devise a credit scoring system utilizing variety of alternative data – demographic and past transnational information of Consumers.


Program Features
Our program focus on enhancing experiential learning using the real datasets and providing a basis for real-world applications. One of the goals of this program is to impart required skill for the participants to built successful careers in Artificial Intelligence.


Applied Artificial Intelligence

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Alumni Connect

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