Course Description
About Artificial Intelligence (AI) Training
Artificial Intelligence (AI) is the next big thing in the technology field and a large number of organizations are already implementing AI and the demand for professionals in AI is growing at an amazing speed. Artificial Intelligence (AI) course with ExcelR will provide you with a wide understanding of the concepts of Artificial Intelligence (AI) to make computer programs to solve problems and achieve goals in the world.
What is Artificial Intelligence (AI)?
Artificial Intelligence (AI) makes computers to perform tasks such as speech recognition, decision-making and visual perception which normally requires human intelligence that aims to develop intelligent machines.
The basic grounding in the ExcelR’s practices in AI is likely to become valuable in the field of business and profession. This course is intended to cover the concepts of Artificial Intelligence from the basics to advanced implementation.
What Are The Course Objectives?
Artificial Intelligence (AI) is becoming smarter day by day in all business functions to elevate performances. AI is used widely in gaming, media, finance, robotics, quantum science, autonomous vehicles, and medical diagnosis. AI technology is a crucial prerequisite in much of the digital transformation taking place today as organizations position themselves to capitalize on the ever-growing amount of data being generated and collected.
To build a successful career in Artificial Intelligence (AI), this course is intended to give a complete understanding of Artificial Intelligence concepts. This course enables you to get practical, hands-on experience to ensure hassle-free execution of real-life projects. This AI course leverages world-class industry expertise in making you professional data science experts.
ExcelR familiarises you with the basic terminologies, problem-solving, and learning methods of AI and also discuss the impact of AI
What Skills Will You Learn?
In this Artificial Intelligence (AI) course, you will be able to
- Understand the basics of AI and how these technologies are re-defining the AI industry
- Learn the key terminology used in AI space
- Learn major applications of AI through use cases
Who Should Take This Course?
ExcelR’s course on Artificial Intelligence (AI) gives you the basic knowledge of Artificial Intelligence. This course doesn’t need any programming skills and is best suited for
- Management and Non-technical participants
- Students who want to learn Artificial Intelligence
- Newbies who are not familiar with AI or its implications
Course Curriculum
- Basic Concept
- ML and AI introduction
- Applications of ML and AI
- Python
- Basic Programming
- NLP Libraries - Spacy & Gensim
- OpenCV & Tensorflow, Keras
- Basic Statistics
- Sampling & Sampling Statistics
- Inferential Stats : Hypothesis Testing
- Calculus
- Derivatives
- Optimization
- Linear Algebra
- Function
- Scalar-Vector-Matrix
- Vector Operation
- Probability
- Space
- Probability
- Distribution
- Unsupervised
- Unsupervised K-Means & Hierarichal Clustering
- Linear Regression
- Logistic Regression
- Evaluation Metrics
- Train,Test & Validation Distribution
- Supervised
- Gradient Descent
- Decision Tree & KNN
- Random Forest | Bagging & Boosting
- Introduction
- Intro
- Deep Learning Importance [Strength & Limiltation]
- SP | MLP
- Feed Forward & Backward Propagation
- Neural Network Overview
- Neural Network Representation
- Activation Function
- Loss Function
- Importance of Non-linear Activation Function
- Gradient Descent for Neural Network
- Practical Aspect
- Train, Test & Validation Set
- Vanishing & Exploding Gradient
- Dropout
- Regularization
- Optimization
- Bias Correction
- RMS Prop
- Adam,Ada,AdaBoost
- Learning Rate
- Tuning
- Softmax
- Image preprocessing
- Introduction to Computer Vision ,Image, image transformation, filters, noise removal, edge detetction, non-max suppression , hysterisis
- Advanced CNN concepts -1
- "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 concepts -2
- Advanced CNN models applications, face detection and recognition, different techniques in face recognition, style transfer
- Speech Processing
- "Introduction, Automated Speech Recognition (ASR) "
- Speech Synthesis
- text to speech conversion, voice assistant devices, building alexa slkills
- Autoencoders & Decoders
- Basics of autoencoders, different types of autoencoders, applications with examples , variational autoencoders, intro to Gen AI
- Generative Adverserial Networks (GAN's)
- GAN basics and foundations, upsampling , GAN models, evaluate GAN Models, inception score, frechet inception distance, GAN loss functions
- GAN's different types
- Conditional GAN, Info GAN, Auxillary GAN etc, applications
- GAN use cases
- Image translation applications, cycle GAN concepts and implemenations
- Reinforcement Learning
- Intro to RL, Q learning, Exploration , exploitation
- Reinforcement learning applications
- Work with deep RL libraries, openai gym library, policy gradient concepts, Actor-critic methods, Proximal policy Optimization (PPO) and related concepts
- Forecasting deep learning
- ARIMA, Deep learning models for forecasting (RNN, LSTM , Transformer applications)
- Basic NLP concepts & models
- "Introduction to Text Mining,VSM, word embeddings applications, RNN , GRU, LSTM models, Intro to Transformers, Attention (Elmo, BERT , T5)"
- "Text Mining & NLP applications, Web Scraping"
- "Word clouds and Doucument Similarity using cosine similarity, Named Entity Recognition, machine translation using hugging face libraries, Emotion Mining using different libraries, web scraping"
- Naive Bayes
- "Text classification using Naïve Bayes, frequqentists vs bayesian , apriori, posteriori distributions Bayesian estimators: posterior mean, posterior median"
- "Advanced NLP models , Generative AI using LLM's"
- "Intro to Transformers & Attention (Single Head,Multi Head) , pretrained 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 of GPT etc, GPT progress, calling OPENAI api's , LLM playgrounds Intro to Different types of Transformer sequence to sequence models-BART, T5"
- Understand the working of LLMs
- LLM, Use Cases, Text Generation, Chatbot Creation, Foundations of Generative Models & LLM, Generative Adversarial Networks (GANs), Autoencoders in Generative AI, Significance of Transformers in AI, "Attention is All You Need" - Transformer Architecture, Reinforcement Learning, RLHF
- Real World Applications and Case Studies
- Real-world applications and case studies of LLMs
- Fine Tuning and Evaluating LLMs
- Instruction fine-tuning, Fine-tuning on a single task, Multi-task instruction fine-tuning, Model evaluation, Benchmarks, Parameter efficient fine-tuning (PEFT), PEFT techniques 1: LoRA, PEFT techniques 2: Soft prompts, Lab 2 walkthrough
- Evaluation Matrix
- Rouge1, BLEU, Meteor, CIDEr
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ExcelR is a training and consulting firm with its global headquarters in Houston, Texas, USA. Alongside to catering to the tailored needs of students, professionals, corporates and educational institutions across multiple locations, ExcelR opened its offices in multiple strategic locations such as Australia, Malaysia for the ASEAN market, Canada, UK, Romania taking into account the Eastern Europe and South Africa. In addition to these offices, ExcelR believes in building and nurturing future entrepreneurs through its Franchise verticals and hence has awarded in excess of 30 franchises across the globe. This ensures that our quality education and related services reach out to all corners of the world. Furthermore, this resonates with our global strategy of catering to the needs of bridging the gap between the industry and academia globally.
