CAAI is an intensive application oriented, real-world scenario-based program in Artificial Intelligence (AI). This is an intensive skill oriented, practical training program to build AI and ML based Applications using various techniques.
It is designed to give the participant enough exposure to the variety of use cases that utilizes analytic techniques learned using this course.
Best Suited For
- Bachelor’s/Master’s degree.
- Comfortable with Maths and Stats.
- Working experience of at least an year.
- 60+ Contact Lectures.
- 60+ Lab, Exercises and Tutorials.
- 240+ Contact Hours
” AI is an unequivocally transformational Technology! We wanted to provide an educational opportunity in Hyderabad in this cutting-edge technology! This is a value-oriented program that provides an experiential learning in an Instructor led sessions with a healthy teacher student ratio. We are confident that our world-class professors and superior training material, we would contribute significantly to nurturing exceptional talent in this hyper emerging field.”
N V Ramana Rao (Director, NIT Warangal)
” Given our experience of delivering over 10,000 hours of various off-campus technical development programs in the areas of Big Data, Statistical Learning, Machine Learning and Neural Networks, we have put together a “best coverage” program that addresses most relevant topics in AI and ML for real-world scenarios. Our approach is also unique because of our emphasis on Statistics approaches and their importance while solving real-life problems. Our program is shaped by extensive discussions with the top most academicians on our Academic Board and Industry stalwarts. ”
Prof. R B V Subhramanyam (Head, Dept of CSE, NIT Warangal)
” Learning AI is a journey of paradigm change for most people! The “process” and “rule-based” thinking generally comes in the way of “data” and “probabilistic” thinking! Our lectures, readings, discussions, and assignments will teach the participant how to apply disciplined, creative methods to ask better questions, gather and process data, create appropriate models and use relevant algorithms. Our program focuses achieving this change of mindset! “
Surya Putchala (CEO, ZettaMine)
your learning Journey
To build successful and rewarding careers requires continuous learning; particularly in the fast moving fiels such as AI. In order to give a kick-start towards that goal, we have put together a 4 facet, accelerated and fully supported learning program that has 4 facets indicating Knowledge acquisition, achieving skill, application of the skills and ability to compete in the modern workplace.
The ability to learn and assimilate large quantities of information, particularly for participants with various backgrounds is a challenge. However, our pedagogical method follows Bloom’s methodology and makes learning experiential, immersive and fun!
- Superior Training material and well-qualified faculty.
- Exposure to use cases in Retail and Financial Services.
- Exceptional Classroom and Lab Facilities
The program uses open-source tool kits, towards hands-on sessions and work-through examples. At the end of this program, we expect the participants to have a good understanding of the methods, methodologies and techniques and tools.
- Extensive Labs, Exercises and Tutorial session
- Work on real-world and Large Datasets
- Utilize GPU enabled Computing Infrastructure.
The emphasis throughout is on making practical contributions to real decisions that organizations will and should make. We will help you explore how various analytical components come together.
- Apply AI/ML methods, techniques and tools immediately.
- Create and implement AI/ML solutions to various use cases.
- Choose from our portfolio of 72 real-life Capstone projects
In order to master AI/ML, it is essential to benchmark one with their peers. Since, the field is becoming competitive, in order to thrive in their careers, the participant needs to continuously learn and compete with his/her peer group!
- Tackle Kaggle problems with assistance from Mentors
- Learn best practices to participate in Hackathons
- Publish the Kernels in Kaggle and Github.
AI/ML is a multidisciplinary field that draws from a range of disciplines such as statistics, mathematics and Computer Science. An AI/ML Professional should feel at ease to build the algorithms necessary, work with various data sources (often in disparate forms) and an innate ability to ask the incisive questions, model and find the right answers. Our syllabus addresses the essential AI/ML landscape, with a focus on learning how to apply AIML techniques to uncover, enrich, and answer questions facing industries today!
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
- CNNs – Object detection and Image Classification
- GANS – Style transfer Apps
- Emerging NN architectures
- 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.
In manufacturing, the finished products are physically inspected for quality issues. Surface defect detection is an interesting application of Deep learning. It has the potential to save the cost of quality by ensuring 100% of the products or goods are inspected through automated means. Many analytical approaches can be adopted for solving this challenging problem such as Machine learning, feature extraction, and neural networks. The dataset based on real-world quality control in a manufacturing unit.
The CAAI program utilizes state-of-the-art open source software platforms that gives participants an exposure to variety of tools that are universally utilized in enterprises across the world.
We, at the E&ICT Academy is focussed on building contemporary skills. In this applied AI program, we will use open source tools, lays emphasis on hands-on sessions and work-through examples. At the end of this program, we expect the participants to have a good understanding of the methods, methodologies and techniques and tools that can be put to use immediately. Our participants are exposed to the latest platforms and tools like using Jupyter Notebook, using Neural networks using Keras and Tensorflow, learning various libraries in the python eco-system and packages in the R ecosystem.
Our mentors are from NIT Warangal and others Institutes or Organizations of repute. They bring decades of experience in Analytics and related fields through Academic and Consulting Projects. They either hold positions of high responsibilities or leaders in their fields of expertise. Thay all have multiple academic/industry publications. They all have advanced degrees from Tier1 institutions in India and abroad.
- Block your seat for Rs. 2,000/- * May qualify for registration waiver.
- Provide professional details to qualify for the program.
- Registration fee will be refunded for those who do not qualify.
- Fees : Rs. 68,000/- + GST * * Avail Referral bonus, corporate discounts, bulk pay incentives. Check with our Program Coordinator.