ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
Embark on an immersive journey into the dynamic realm of our Artificial Intelligence course with our comprehensive program. Designed for both beginners and experienced professionals, our Artificial Intelligence course offers a deep dive into the core concepts, theories, and practical applications of Artificial Intelligence course. From understanding the fundamentals of machine learning algorithms to exploring advanced topics such as deep learning and reinforcement learning, you’ll develop a robust understanding of Artificial Intelligence (AI) technologies. Through a combination of lectures, hands-on exercises, and real-world projects, you’ll gain valuable experience in implementing Artificial Intelligence course solutions across various domains, including healthcare, finance, marketing, and more.
Our expert instructors, who are industry veterans with extensive experience in Artificial Intelligence (AI) research and development, will guide you through the intricacies of our Artificial Intelligence course, providing valuable insights, tips, and best practices along the way. You’ll also have the opportunity to collaborate with peers, participate in group discussions, and receive personalized feedback to enhance your learning experience.
By the end of our Artificial Intelligence course, you’ll emerge as a proficient Artificial Intelligence (AI) practitioner, equipped with the skills, knowledge, and confidence to tackle complex Artificial Intelligence (AI) challenges and drive innovation in your organization. Whether you’re looking to advance your career, launch a startup, or simply satisfy your curiosity about Artificial Intelligence (AI), our Artificial Intelligence course will empower you to achieve your goals.
Join us on this transformative journey and become part of a vibrant community of Artificial Intelligence (AI) enthusiasts, innovators, and thought leaders. Don’t miss out on the opportunity to unlock the full potential of our Artificial Intelligence course and shape the future of technology. Enroll now and take the first step towards becoming an Artificial Intelligence (AI) expert!
Course Objectives
THINKWorks courses equip students, teachers, teacher educators and other education practitioners with the skills and knowledge that they need in today’s world. Courses include Digital well being & Cybersecurity, Coding works – Python Programming & Public Speaking & Presentation skills THINKWorks is India’s first UpSkilling Academy for school children which were founded to bridge the skill gap between academia and industry.
Things you will learn
We help our students at every stage, from the start of a course to the actual skill-building technique. Our curriculum covers the following.
Programming
Python
- Basic Programming
- NLP Libraries – Spacy & Gensim
- OpenCV & Tensorflow, Keras
Math Foundation
Space
Derivatives Optimization
Function Scalar-Vector-Matrix Vector Operation
Sampling & Sampling Statistics Inferential Stats: Hypothesis Testing
Calculus
Probability
Linear Algebra
Basic Statistics
Machine Learning & Ensemble Methods
K-Means & Hierarchical Clustering Linear Regression Logistic Regression Train, Test & Validation Distribution Gradient Descent Decision Tree & KNN Random Forest | Bagging & Boosting
Intro to Neural Network & Deep Learning
Intro Deep Learning Importance [Strength & Limiltation] SP | MLP
Neural Network Overview Neural Network Representation Activation Function Loss Function Importance of Non-linear Activation Function Gradient Descent for Neural Network
Introduction
Feed Forward & Backward Propagation
Parameter & Hyperparameter
Train, Test & Validation Set Vanishing & Exploding Gradient Dropout Regularization
Bias Correction RMS Prop Adam, Ada, AdaBoost Learning Rate Tuning Softmax
Practical Aspect
Optimization
Computer Vision
Object detection concepts, Bounding box, object detection models, landmark detection, RCNN, fast RCNN, faster RCNN, mask RCNN, YOLO pre-trained models, transfer learning, segmentation concepts
Advanced CNN models applications, face detection and recognition, different techniques in face recognition, style transfer
Intro duction to Computer Vision, Image, Image transformation, filters, noise removal, edge detection, non-max suppression, hysteresis
Image preprocessing
Advanced CNN concepts -1
Advanced CNN concepts -2
Speech Analytics
Introduction, Automated Speech Recognition (ASR)
Text-to-speech conversion, Voice Assistant devices, building Alexa skills
Speech Synthesis
Speech Processing
Generative AI
Conditional GAN, Info GAN, Auxillary GAN, etc, applications
Image translation applications, cycle GAN concepts and implementations
Basics of autoencoders, different types of autoencoders, applications with examples, variational autoencoders, intro to Gen AI
GAN basics and foundations, upsampling, GAN models, evaluate GAN Models, inception score, Fréchet Inception Distance, GAN loss functions
GAN use cases
GAN’s different types
Autoencoders & Decoders
Generative Adversarial Networks (GANs)
Reinforcement Learning
Intro to RL, Q learning, Exploration, Exploitation
Work with deep RL libraries, OpenAI gym library, Policy Gradient Concepts, Actor-critic methods, Proximal Policy Optimization (PPO) and related concepts
Reinforcement Learning
Reinforcement learning applications
RNN and LSTM
ARIMA, Deep learning models for forecasting (RNN, LSTM, Transformer applications)
Forecasting deep learning
Neural Language Processing
Word clouds and Document similarity using cosine similarity, Named Entity Recognition, machine translation using hugging face libraries, Emotion Mining using different libraries, web scraping
Text classification using Naive Bayes, Frequentists vs Bayesian, Apriori, posterior distributions Bayesian estimators: posterior mean, posterior median
Introduction to Text Mining, VSM, Word Embedding Applications, RNN, GRU, LSTM models, Intro to Transformers, Attention (Elmo, BERT, T5)
Intro to Transformers & Attention (Single Head, Multi Head), pre-trained models (GPT, BERT, BART, T5) models with applications, examples using python Intro to Different types of Transformer encoder models- Basic BERT, RoBERTa, DistilBERT, etc. Intro to Different types of Transformer decoder models- GPT, GPT2, other variants ofGPT, etc., GPT progress, calling OPENAI APIs, LLM playgrounds Intro to Different types of Transformer sequence to sequence models- BART, T5
Naive Bayes
Basic NLP concepts & models
Text Mining & NLP applications, Web Scraping
Advanced NLP models, Generative AI using LLM’s
Value-Add Courses
Python
Mysql
ChapGPT
Prompt Engineering
We’re here to answer any questions you might have!
We look forward to hearing from you
