Advanced AI and Generative AI Program

skillzrevo

Last Update January 3, 2025

4 already enrolled

About This Course

This Program offers a deep dive into fundamental AI technologies, Python programming languages and essential libraries for machine learning image processing, etc. Participants will learn cutting-edge tools such as Hugging Face Transformers, GPT, DALL-E 2.0, MidJourney, GANs, RAG, and LanguageChain. The curriculum integrates theoretical understanding with real-world use cases to develop a skills-rich ecosystem capable of utilizing AI and generative technologies at an advanced level.

Curriculum

51 Lessons

Python

Introduction to Python, Why Python, Variables, Data Types, Type castings, Strings, Indexing03:00:00

Operators and Conditional Statements, Looping Statements and it’s Control Statement03:00:00

Lambda Functions, *args, **kwargs, Functions00:00

Data Structures – List, Tuple and List Comprehensions00:00

Data Structures – Set and Dictionaries00:00

Classes, Objects and Constructors, Inheritance00:00

Polymorphism, Abstraction and Encapsulation00:00

Connecting & Establishing connections to databases, Executing SQL Queries, ORM, Working with NoSQL Databases00:00

Introduction of Numpy, and Pandas00:00

Introduction of Seaborn and Matplotlib00:00

Loops, Functions, and Essential Conceptst

Patient Visit Analysis

Statistics

Machine Learning

Project Session 01

Deep Learning

Computer Vision

Natural Language Processing

Reinforcement Learning

Project Session 02

Introduction to Gen AI & Hugging face Transformers Platform

Language Models and Transformer Models

Large Language Models (LLMs)

LangChain, AI Application Stack and Ethical Considerations

Project Session 03

Your Instructors

skillzrevo

0/5

39 Courses

0 Reviews

439 Students

Advanced AI and Generative AI Program

About This Course

Curriculum

Python

Introduction to Python, Why Python, Variables, Data Types, Type castings, Strings, Indexing03:00:00

Operators and Conditional Statements, Looping Statements and it’s Control Statement03:00:00

Lambda Functions, *args, **kwargs, Functions00:00

Data Structures – List, Tuple and List Comprehensions00:00

Data Structures – Set and Dictionaries00:00

Classes, Objects and Constructors, Inheritance00:00

Polymorphism, Abstraction and Encapsulation00:00

Connecting & Establishing connections to databases, Executing SQL Queries, ORM, Working with NoSQL Databases00:00

Introduction of Numpy, and Pandas00:00

Introduction of Seaborn and Matplotlib00:00

Loops, Functions, and Essential Conceptst

Patient Visit Analysis

Statistics

Introduction to Statistics, Descriptive Statistics, Sample, Population, Major of Central Tendency, Standard Deviation00:00

Variance, Range, IQR, Outliers, Correlation, Covariance, Skewness, Kurtosis, Probability00:00

Probability, Probability distributions, Central Limit Theorem, Binomial and Poisson Distribution00:00

Normal Distribution, Type I & Type II Error00:00

T-test, Z-test, Hypothesis, Testing Interview Questions00:00

Machine Learning

Introduction to ML, Types of variables, Encoding, Normalization, Standardization, Types of ML, Linear Regression00:00

Linear Regression, Logistic Regression, SVM, KNN, Naïve Bayes, Decision Tree, Random Forest00:00

Mean Absolute Error, Mean and Root Mean Square Error, Confusion Matrix, R2 Score, Adjusted R2 Score,F1 Score00:00

Classification Report, AUC ROC, Accuracy, Ensemble Techniques, Random Forest, Xgboost00:00

Unsupervised Machine Learning, PCA, Clustering, k-Means Clustering and Hierarchical clustering00:00

Project Session 01

Project Session: Data Analysis & Model Building with Machine Learning00:00

Deep Learning

Introduction to Neural Network, Foreward Propagation, Activation Function00:00

Activation Function(Linear, Sigmoid, Relu, Leaky Relu), Optimizers, Gradient Descent, Stochastics Gradient Descent00:00

Mini batch Gradient Descent, Adagrad, Padding, Pooling, Convolution, Checkpoints and Neural Networks Implementation00:00

Introduction to Time Series Analysis, Various components of the TSA, Decomposition Method (Additive Method and Multiplicative)00:00

ARMA and ARIMA00:00

Computer Vision

Introduction to Image Processing, Feature Detection, OpenCV00:00

Convolution, Padding, Pooling & its Mechanisms00:00

Forward Propagation & Backward Propagation for CNN00:00

“CNN Architectures like AlexNet, VGGNet, InseptionNet, ResNet, Transfer Learning”00:00

Natural Language Processing

“Introduction to Text Mining, Text Processing using Python and Introduction to NLTK00:00

Sentiment Analysis, Topic Modeling (LDA) and Name- Entity Recognition00:00

BERT, Text Segmentation, Text Mining, Text Classification00:00

Automatic Speech Recognition, Introduction to Web Scraping00:00

Reinforcement Learning

RL Framework, Component of RL Framework, Exampes of Systems00:00

Types of RL Systems, Q-Learning00:00

Project Session 02

Project Session: Image & Text Analysis, with Intelligent Decision-Making capabilities using Advanced Deep Learning & NLP Techniques00:00

Introduction to Gen AI & Hugging face Transformers Platform

Intro. to AI, Hype vs. Reality, Business Applications, Ethical Considerations, Intro to Gen AI, From Text Generation to Multi-modal, Potential & Challenges00:00

Intro to Open source Hugging-face Transformers Platform, Review of NLP Basics & Text Pre-processing, Introduction to NLP Concepts: Language Representations, Tokenization, Part-of-Speech Tagging, Text Preprocessing00:00

Feature Engineering: Normalization, Stemming, Lemmatization, Stop Word Removal, Understanding key NLP Applications using Hugging-face platform00:00

Sentiment analysis, Sentence classification, Generating text, Extracting an answer from text00:00

Language Models and Transformer Models

Understanding Language Models, Probability-based Language Models, Unsupervised Learning Language representations, Introduction to Transformer Models, What are Transformer Models00:00

Types of Models: Encoder–Decoder, Decoder only, Attention mechanism, Tasks that Transformer Models can do: translation, text summarization, Q&A, text generation, Zero shot, few shot text classification00:00

Large Language Models (LLMs)

Introduction to Large Language Models (LLMs), – Structure of popular models – Types of Models: text to text, text to image, text to video, Multi-modal00:00

Other types of Generative AI Algorithms, – GANs, VAEs, Diffusion Models, Mixture of Experts, – Different models available currently for image ( DALLE-2, Mid-journey)00:00

Hands on Practice of NLP tasks using Hugging-face library and Opensource Language Models such as Bloom for fine tuning a LLMs, zero and few shot Classification – Applications of Generative AI in business00:00

Customer Insights & Sentiment Analysis, Personalized Marketing & Content Creation, Chat-bots: Automating Customer Service and Support, Document Processing Automation00:00

LangChain, AI Application Stack and Ethical Considerations

LangChain, Applied use case for Gen AI – Hands on Exercise, Designing a custom chat-bot, Data Analytics using Gen AI Model such as Open AI API00:00

AI Application Stack: Infrastructure & Foundation Layer, Overview of AI Infrastructure: Cloud Platforms, GPU, and Distributed Computing, Setting up an AI environment for Generative Models, Infrastructure considerations for scalable AI applications, RAG00:00

Project Session 03

To build practical Applications using cutting-edge NLP techniques with a focus on Generative AI Models and Hugging Face00:00

Your Instructors

skillzrevo

Generative AI Program Batch SkillzRevo

Advanced Certification in Data Science & Gen AI with Visualizations Tools

Login