AI-ML Center of Excellence
Overview
Go Yotta Academy, a mission dedicated to providing comprehensive education and training in the field of Artificial Intelligence (AI) and Machine Learning (ML). Our academy offers a wide range of courses and programs that cover essential competencies required to excel in the world of AI/ML.
Mathematics for AI/ML: Our academy places strong emphasis on the mathematical foundations of AI/ML. Courses cover key topics such as linear algebra, calculus, probability, statistics, and optimization. These foundational concepts are essential for understanding and applying advanced AI/ML algorithms and techniques.
Programming Languages for AI/ML: We provide in-depth training in the programming languages most widely used in the AI/ML domain, such as Python and R. These languages offer powerful libraries and frameworks that enable efficient development and implementation of AI/ML models.
AI/ML Fundamentals: Our academy offers comprehensive courses covering the fundamental concepts of AI/ML. Students will gain a deep understanding of supervised learning, unsupervised learning, reinforcement learning, neural networks, decision trees, clustering, and regression. These concepts form the backbone of AI/ML and provide the necessary knowledge to build intelligent systems.
Deep Learning: Our deep learning courses delve into advanced neural network architectures. Students will learn about convolutional neural networks (CNNs) for computer vision tasks, recurrent neural networks (RNNs) for sequential data processing, deep belief networks (DBNs) for unsupervised learning, and autoencoders for feature extraction and generation. These techniques are at the forefront of AI/ML research and are used in various applications.
Natural Language Processing: Our academy specializes in training students in natural language processing techniques. Courses cover text preprocessing, sentiment analysis, language modeling, and neural machine translation. These skills are essential for understanding and working with textual data, enabling students to build powerful language-based AI systems.
Computer Vision: Our computer vision courses focus on image preprocessing, object detection, image segmentation, and facial recognition. Students will gain hands-on experience in working with image data and learn how to apply AI/ML algorithms to solve real-world computer vision problems.
Advanced Topics in AI/ML: For students looking to explore cutting-edge advancements, our academy offers courses on topics such as generative adversarial networks (GANs), transfer learning, federated learning, explainable AI, and reinforcement learning. These advanced topics provide a deeper understanding of AI/ML and equip students with the knowledge to tackle complex challenges.
Capstone Project: To reinforce learning and practical application, students will have the opportunity to work on a capstone project. This project allows them to apply the concepts and skills learned throughout the courses to solve a real-world problem. Examples include building recommendation systems, chatbots, or other AI/ML applications.
At Go Yotta academy, we are committed to providing a comprehensive and immersive learning experience. Our courses are designed by industry experts and delivered through a combination of lectures, hands-on exercises, and real-world projects. Join us on this exciting journey into the world of AI/ML and unlock your potential to shape the future of technology.
Curriculum for a typical 6-8 week AI/ML program:
Week 1: Mathematics for AI/ML
- Linear Algebra: Vectors, matrices, operations, transformations
- Calculus: Differentiation, optimization, gradients
- Probability and Statistics: Probability theory, statistical distributions, hypothesis testing
Week 2: Programming Languages for AI/ML
- Introduction to Python: Syntax, data types, control flow
- Data Manipulation with Python: NumPy, Pandas
- Introduction to R: Syntax, data structures, functions
Week 3: AI/ML Fundamentals
- Supervised Learning: Regression, classification, evaluation metrics
- Unsupervised Learning: Clustering, dimensionality reduction
- Reinforcement Learning: Markov decision processes, Q-learning
Week 4: Deep Learning
- Neural Networks: Architecture, activation functions, backpropagation
- Convolutional Neural Networks (CNNs): Image recognition, CNN architectures
- Recurrent Neural Networks (RNNs): Sequence modeling, language generation
Week 5: Natural Language Processing
- Text Preprocessing: Tokenization, stemming, stop words
- Sentiment Analysis: Text classification, sentiment lexicons, machine learning approaches
- Language Modeling: N-grams, language generation, recurrent neural networks for text generation
Week 6: Computer Vision
- Image Preprocessing: Image resizing, normalization, filtering
- Object Detection: Sliding window, region-based convolutional neural networks
- Image Segmentation: Thresholding, region-growing, deep learning approaches
- Facial Recognition: Face detection, feature extraction, face recognition algorithms
Week 7: Advanced Topics in AI/ML
- Generative Adversarial Networks (GANs): Architecture, training, applications
- Transfer Learning: Fine-tuning, feature extraction, domain adaptation
- Federated Learning: Decentralized model training, privacy-preserving techniques
- Explainable AI: Interpretable models, model explanations, fairness and bias
Week 8: Capstone Project
- Students work on a real-world project applying the concepts and techniques learned throughout the program. They can choose a project related to recommendation systems, chatbots, computer vision tasks, or any other AI/ML application of interest.