Beginner MLOps

Master TensorFlow 2 and Keras. ANNs, CNNs, RNNs, GANs, deployment.

This bootcamp on Udemy focuses on the operational aspects of deploying large language models. It covers CI/CD, Docker, Kubernetes, and monitoring for production LLM deployment, which are essential skills for managing and optimizing the cost and performance of LL Ms at scale.

This course provides a comprehensive, hands-on introduction to machine learning on the Google Cloud Platform, with a specific focus on Vertex AI. Students will learn about various GCP services, including compute, storage, and databases, before diving into machine learning workflows. The curriculum covers building and deploying models using GCP's AutoML for tabular, image, and text data, as well as custom model training and deployment on the AI Platform and Vertex AI. The course is designed to equip learners with the practical skills needed to create and manage machine learning pipelines on Google Cloud.

Welcome to the course "Modern Computer Vision & Deep Learning with Python & PyTorch"! Imagine being able to teach computers to see just like humans. Computer Vision is a type of artificial intelligence (AI) that enables computers and machines to see the visual world, just like the way humans see and understand their environment. Artificial intelligence (AI) enables computers to think, where Computer Vision enables computers to see, observe and interpret. This course is particularly designed to provide a comprehensive, hands-on experience in applying Deep Learning techniques to major Computer Vision problems including Image Classification, Semantic Segmentation, Instance Segmentation, and Object Detection. In this course, you'll start with an introduction to the basics of Computer Vision and Deep Learning, and learn how to implement, train, test, evaluate and deploy your own models using Python and PyTorch for Image Classification, Image Segmentation, and Object Detection. Computer Vision plays a vital role in the development of autonomous vehicles. It enables the vehicle to perceive and understand its surroundings to detect and classify various objects in the environment, such as pedestrians, vehicles, traffic signs, and obstacles. This helps to make informed decisions for safe and efficient vehicle navigation. Computer Vision is used for Surveillance and Security using drones to track suspicious activities, intruders, and objects of interest. It enables real-time monitoring and threat detection in public spaces, airports, banks, and other security-sensitive areas. Today Computer Vision applications in our daily life are very common including Face Detection in cameras and cell phones, logging in to devices with fingerprints and face recognition, interactive games, MRI, CT scans, image guided surgery and much more. This comprehensive course is especially designed to give you hands-on experience using Python and PyTorch coding to build, train, test and deploy

Are you ready to master Deep Learning skills?Deep Learning is a technology using which we can solve highly computational problems such as Image Processing, Image Classification, Image Segmentation, Image tagging, sound classification, video analysis, etc.Deep Learning is becoming a buzzword these days, and If you want to learn Deep Learning then It is very important for you that you should have a proper plan regarding that.Before Learning Deep Learning you must have learned Machine Learning and must possess good knowledge of the Python programming language.If you want to build super-powerful applications in Deep Learning. Then, you are at the right place.This course will provide you with in-depth knowledge on a very hot topic i.e., Deep Learning.The purpose of this course is to provide you with knowledge of key aspects of Deep Learning without any intimidating mathematics and in a practical, easy, and fun way. The course provides students with practical hands-on experience using real-world datasets.This course will cover the following topics:-1. Deep Learning (DL).2. Artificial Neural Network (ANNs).3. Convolutional Neural Network (CNNs).4. Recurrent Neural Network. (RCN)5. Learn to Implement the LSTMs.This course will take you through the basics to an advanced level in all the mentioned four topics.After taking this course, you will be confident enough to work independently on any projects on these topics.There are lots and lots of exercises for you to practice In this Deep Learning Course and also a  5 Bonus Deep Learning Project "Stock Market Prediction", "Fruits Identification System", "Face Expression Recognizer", "Detecting Pneumonia from Chest X-rays", and "Optimizing Crop Production".In this Optimizing Crop Production, you will learn about Precision Farming using Data Science T

Image classification models find themselves in different places today, like farms, hospitals, industries, schools, and highways,... With the creation of much more efficient deep learning models from the early 2010s, we have seen a great improvement in the state of the art in the domain of image classification.In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step approach. We shall start by understanding how image classification algorithms work, and deploying them to the cloud while observing best practices. We are going to be using TensorFlow 2 (the world's most popular library for deep learning, built by Google) and Hugging Face You will learn:The Basics of TensorFlow (Tensors, Model building, training, and evaluation)Deep Learning algorithms like Convolutional neural networks and Vision Transformers Evaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)Mitigating overfitting with Data augmentation Advanced TensorFlow concepts like Custom Losses and Metrics, Eager and Graph Modes and Custom Training Loops, Tensorboard Machine Learning Operations (ML Ops) with Weights and Biases (Experiment Tracking, Hyperparameter Tuning, Dataset Versioning, Model Versioning)Binary Classification with Malaria detection Multi-class Classification with Human Emotions Detection Transfer learning with modern ConvNets (Vggnet, Resnet, Mobilenet, Efficientnet)Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)If you are

Sentiment analysis and machine translation models are used by millions of people every single day. These deep learning models (most notably transformers) power different industries today. With the creation of much more efficient deep learning models, from the early 2010s, we have seen a great improvement in the state of the art in the domains of sentiment analysis and machine translation.In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step approach. We shall start by understanding how to process text in the context of natural language processing, then we would dive into building our own models and deploying them to the cloud while observing best practices. We are going to be using TensorFlow 2 (the world's most popular library for deep learning, built by Google) and Hugging Face You will learn:The Basics of TensorFlow (Tensors, Model building, training, and evaluation).Deep Learning algorithms like Recurrent Neural Networks, Attention Models, Transformers, and Convolutional neural networks.Sentiment analysis with RNNs, Transformers, and Hugging Face Transformers (Deberta)Transfer learning with Word2vec and modern Transformers (GPT, Bert, ULmfit, Deberta, T5...)Machine translation with RNNs, attention, transformers, and Hugging Face Transformers (T5)Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)If you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals!This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much em

Unlock the power of image- and video-based AI in 2025 with 20+ real-time projects that guide you from foundational theory to fully functional applications. Designed for engineering and science students, STEM graduates, and professionals switching into AI, this hands-on course equips you with end-to-end computer vision skills to build a standout portfolio.Key Highlights:Environment Setup & Basics: Install Python, configure VS Code, and master OpenCV operations—image I/O, color spaces, resizing, thresholding, filters, morphology, bitwise ops, and histogram equalization.Core & Advanced Techniques: Implement edge detection (Sobel, Canny), contour/corner/keypoint detection, texture analysis, optical flow, object tracking, segmentation, and OCR with Tesseract.Deep Learning Integration: Train and deploy TensorFlow/Keras models (Efficient Net-B0) alongside YOL Ov7-tiny and YOL Ov8 for robust detection tasks.GUI Development: Build interactive Tkinter interfaces to visualize live video feeds, detection results, and system dashboards.20+ Hands-On Projects Include:Smart Face Attendance with face enrollment, embedding extraction, model training, and GUI integration.Driver Drowsiness Detection using EAR/MAR algorithms and real-time alert dashboards.YOLO Object & Weapon Detection pipelines for live inference and visualization.People Counting & Entry/Exit Tracking with configurable line-coordinate logic.License-Plate & Traffic Sign Recognition leveraging Roboflow annotations and custom model training.Intrusion & PPE Detection for workplace safety monitoring.Accident & Fall Detection

Embark on a comprehensive journey into the world of AI agents with Lang Graph. This course is designed to guide you from fundamental concepts to advanced techniques, equipping you with the skills to build sophisticated AI systems. Starting with the core principles, you'll learn about graphs, nodes, edges, and states, and see how they form the foundation of Lang Graph workflows. The course begins with constructing a basic agent, allowing you to grasp the essentials through hands-on practice.Next, you'll dive deeper by building a News Writer Agent, enhancing your understanding by integrating state and tools into your agents. The focus will be on practical applications, ensuring you can visualize and test your agents effectively. Finally, the course introduces advanced techniques, including reflection, human-in-the-loop processes, checkpointers, and threads. You'll also learn to incorporate custom tools, adding versatility and functionality to your agents. Whether you're a beginner or looking to advance your skills, this course provides a structured, step-by-step approach to mastering AI agent development with Lang Graph.The goal of this course is to equip you with the understanding and skills you need to build your own agents. There are plenty of off-the-shelf agents available via Lang Graph and other resources. However, in our experience, when building agents for production you will need to be able to customize. At the end of this course, it is our goal to make sure that you are capable of building your own custom workflows in Lang Graph.Note: Prior python programming experience and some experience with LangChain are required for this course.

Unlock the Power of AI: From Beginner to Advanced Machine Learning &amp; Deep Learning Projects Are you ready to dive into the world of Artificial Intelligence and master Machine Learning and Deep Learning? Whether you're just starting or want to expand your AI skills, this comprehensive course is designed to guide you through hands-on projects that you can use to showcase your abilities in the real world.Key Highlights of the Course:Hands-On, Project-Based Learning: This is not just a theory-heavy course. You’ll be actively building and deploying AI models that solve real-world problems. Each module introduces a new project, ensuring you gain practical experience while learning.Perfect for Beginners to Experts: Start with the basics and move towards advanced concepts at your own pace. Whether you're new to AI or looking to deepen your knowledge, this course will meet you where you are and help you grow.Practical AI Applications: Learn to apply AI in fields like image classification, natural language processing (NLP), recommendation systems, and more, giving you a diverse skillset that can be applied to various industries.Master Deep Learning: Learn cutting-edge techniques like neural networks, CNNs (Convolutional Neural Networks), and RNNs (Recurrent Neural Networks) to handle complex tasks, opening up exciting opportunities in AI development.Deployment &amp; Scalability: Learn to take your models from development to deployment. Understand how to use cloud platforms and scaling strategies to make your AI solutions accessible and efficient.Collaborative Learning: Engage with fellow learners, share your progress, and collaborate on projects, creating a supportive and dynamic learning environment.Expert Mentorship:<

Are you ready to dive into the fascinating world of object detection using deep learning? In our comprehensive course "Deep Learning for Object Detection with Python and PyTorch", we will guide you through the essential concepts and techniques required to detect, classify, and locate objects in images. Object Detection has wide range of potential real life application in many fields. Object detection is used for autonomous vehicles to perceive and understand their surroundings. It helps in detecting and tracking pedestrians, vehicles, traffic signs, traffic lights, and other objects on the road. Object Detection is used for surveillance and security using drones to identify and track suspicious activities, intruders, and objects of interest. Object Detection is used for traffic monitoring, helmet and license plate detection, player tracking, defect detection, industrial usage and much more.With the powerful combination of Python programming and the PyTorch deep learning framework, you'll explore state-of-the-art algorithms and architectures like R-CNNs, Fast RCNN and Faster R-CNNs. Throughout the course, you'll gain a solid understanding of Convolutional Neural Networks (CNNs) and their role in Object Detection. You'll learn how to leverage pre-trained models, fine-tune them for Object Detection using Detectron2 Library developed by by Facebook AI Research (FAIR).The course covers the complete pipeline with hands-on experience of Object Detection using Deep Learning with Python and PyTorch as follows:Course Break Down:Learn Object Detection with Python and PyTorch Coding Learn Object Detection using Deep Learning Models Introduction to Convolutional Neural Networks (CNNs)Learn RCNN, Fast RCNN, Faster RCNN, Mask RCNN and YOLO8, YOLO11 Architectures Perform Object Detection with Fast RCNN and Faster RCNN Perform Real-time Video Object Detection with YOL Ov8 and YOLO11</

Welcome to Building Machine Learning &amp; NLP Models for Cyber Security course. This is a comprehensive project based course where you will learn how to build intrusion detection system, predict vulnerability score, and classify cyber threat using machine learning models like Random Forest Classifier, Logistic Regression, MLPs Regressor, Decision Tree Regressor, KNN, XG Boost, Naive Bayes, and K Means Clustering. This course is a perfect combination between machine learning and cyber security, making it an ideal opportunity to practice your programming skills while improving your technical knowledge in system security. In the introduction session, you will learn about machine learning and natural language processing applications in cyber security, specifically how it can help to enhance risk management and strengthen overall security. Then, in the next section, we will learn how intrusion detection models work. This section will cover data collections, data preprocessing, feature selection, splitting data into training and testing sets, model selection, model training, detecting intrusion, model evaluation, deployment, and monitoring. Afterward, we will download cyber security datasets from Kaggle, it is a platform that offers many high quality datasets from various sectors. Once everything is all set, then, we will start the project, firstly, we will clean the dataset by removing all missing values and duplicates, after we make sure the data is clean and ready to use, we will start exploratory data analysis, firstly we are going to analyze the relationship between protocol type and intrusion, which will enable us to understand how different communication protocols contribute to intrusion risk, following that, we are also going to analyze intrusion rate for each browser type, which will allow us to uncover potential vulnerabilities associated with specific browsers, then, we are going to calculate the average login attempts and failed logins for both normal and intru

Most LangChain and Lang Graph courses are Python-first. This one is built from the ground up for Java Script &amp; Type Script engineers who want real, shippable agentic systems—not disconnected demos.You’ll build a sequence of end-to-end projects that mirror how modern teams ship AI features: clean Type Script code, clear AP Is, JSON contracts, Lang Graph orchestration, RAG, proper vector stores, and real Next.js frontends wired to real agents.By the end, you’ll know exactly how to go from idea → design → implementation → observability → deployment in the JS ecosystem.Here’s what we’ll cover in Phase 1:Intro &amp; Mindset How this course works, what it is / isn’t, and how to follow.Choosing models (OpenAI / Gemini / Groq / local) smartly for cost, speed &amp; reliability.How all projects connect into a reusable “agent platform” you can extend.Foundations: LangChain, Agents &amp; Flow Modern AI app architecture: UI → orchestration → models → tools → storage.Simple, honest definition of AI agents and real-world use cases.Chains vs agents: when a chain is enough, when an agent is worth it.Where LangChain.js fits, where Lang Graph.js fits, and how they work together.JSON-first mindset teaser: why strings lie and schemas save you.Orientation &amp; “Hello Agent” ProjectTS/Node project setup, tsconfig, env patterns, scripts.Multi-provider setup: OpenAI, Gemini, Groq via a single provider factory.First “Hello Agent” function that runs like a clean backend primitive, not a toy script.LLM Fundamentals: JS

This hands-on course teaches you how to build professional level Generative AI Application, intelligent, autonomous AI Agents using MCP (Model Context Protocol) and modern LLM frameworks.Whether you’re an AI beginner or an experienced developer, this course will take you step-by-step through the tools, strategies, and architectures that power modern GenAI applications.What You’ll Learn:- Introduction to Generative AI and its role in modern development- Introduction to Large Language Models (LL Ms) and how they power intelligent applications- Generative AI Architecture Basics – understand the core components of a Gen AI application- Advanced Gen AI Application Architecture for scalable and modular systems- How to apply the Retrieval-Augmented Generation (RAG) technique for enhanced responses- Choosing the Right Orchestration Framework for building LLM-powered apps- LangChain – A modern framework for LLM orchestration- LangChain Expression Language (LCEL) – Build AI flows with clean, declarative syntax- Deep dive into the LangChain Ecosystem for agents, tools, memory, and chains- Mastering Prompt Engineering – Learn to craft optimal prompts for LL Ms- Level 1 Gen AI Applications – Basic AI-powered tools and assistants- Llama Index – An alternative to LangChain for RAG and LLM app orchestration- LLM Ops (Large Language Model Operations) – Manage and monitor LLM Apps- Level 2 Gen AI Applications – Build intermediate systems with memory, tools, and retrieval- Develop Multimodal Gen AI Applications (text, image, audio integration)- Build and deploy AI Agents &amp; Multi-Agent Systems using orchestration frameworks- Level 3 (Professional) Gen AI Applications – Real-time, scalable, production-ready systems- CI/CD for Gen AI – Deploy your Gen AI apps with automated pipelines- Understand and implement MCP (Model Context Protocol) - Ha

Are you excited about the future of AI where intelligent agents can think, act, and collaborate to solve complex tasks autonomously? Welcome to the Complete Agentic AI Bootcamp with Lang Graph and LangChain — your one-stop course to master the art of building agentic AI applications from scratch!This course is designed to teach you everything you need to know about Agentic AI, Lang Graph, and LangChain — two of the most powerful frameworks for building intelligent AI agents and multi-agent systems.You will start by understanding the fundamentals of Agentic AI — how it differs from traditional AI models, the key components of agents (memory, tools, decision-making), and real-world use cases.We will then dive deep into Lang Graph, a cutting-edge framework that helps you design complex agent workflows using graphs, events, and state transitions. You’ll also learn how to combine LangChain's power with Lang Graph to build production-ready agent applications.Throughout the course, you will build real-world projects step-by-step, including:Creating single intelligent agents with memory and tool-usage capabilities.Designing multi-agent collaboration systems with message passing and shared goals.Implementing autonomous research assistants, task automation bots, and retrieval-augmented generation (RAG) agents.You will not just learn theory — you will build and deploy multiple end-to-end agentic applications, gaining real-world experience in constructing powerful AI systems.By the end of this course, you will have the skills and confidence to create your own AI agents and deploy complex agentic applications for various domains like search, research, task planning, customer support, and beyond.What You Will Learn:Core concepts behind Agentic AI and how intelligent agents operate.Hands-on mastery of Lang Graph and LangChain for b

A warm welcome to the Deep Learning with TensorFlow course by Uplatz.TensorFlow is an end-to-end open-source machine learning / deep learning platform. It has a comprehensive ecosystem of libraries, tools, and community resources that lets AI/ML engineers, scientists, analysts build and deploy ML-powered deep learning applications. The name TensorFlow is derived from the operations which neural networks perform on multidimensional data arrays or tensors. Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain.TensorFlow is a machine learning framework that Google created and used to design, build, and train deep learning models. You can use the TensorFlow library do to numerical computations, which in itself doesn’t seem all too special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or tensors, that are communicated between these edges.In simple words, TensorFlow is an open-source and most popular deep learning library for research and production. TensorFlow in Python is a symbolic math library that uses dataflow and differentiable programming to perform various tasks focused on training and inference of deep neural networks. TensorFlow manages to combine a comprehensive and flexible set of technical features with great ease of use.There have been some remarkable developments lately in the world of artificial intelligence, from much publicized progress with self-driving cars to machines now composing imitations or being really good at video games. Central to these advances are a number of tools around to help derive deep learning and other machine learning models, with Torch, Caffe, and Theano amongst those at the fore. However, since Google Brain went open source in November 20

In this course I will teach you how to use LangChain to build LLM powered Applications and I will be using Open source models from Hugging Face What is LangChain?LangChain serves as a framework aimed at streamlining the development of applications utilizing Large language models. Functioning as a language model integration framework, LangChain's applications align closely with those of language models, spanning document analysis, summarization, chatbots, and code analysis.What is an LLM?A Large Language Model (LLM) is a type of artificial intelligence model that is trained on a vast amount of text data. It’s designed to generate human-like text based on the input it receives.In this course, I will be using LL Ms such as Llama 2 7B and Mistral 7B.What is LCEL?LangChain Expression Language (LCEL) emerges as a declarative method within the LangChain framework, enabling effortless composition of chains. From its inception, LCEL prioritizes seamless transition from prototypes to production, accommodating a spectrum of complexities, from straightforward "prompt + LLM" sequences to intricate chains comprising hundreds of steps. Noteworthy features encompass streaming support for optimal time-to-first-token, asynchronous capabilities for versatile API usage, and optimized parallel execution for reduced latency. LCEL further offers configurations for retries, fallbacks, and access to intermediate results, enhancing reliability and debugging.In this course you learn - LangChain Basics - LangChain Expression Language- Chains- Memory- Agents and Tools- RAG etc Disclaimer: In this course I won't be using OpenAI API instead I would be using Open source models from Hugging Face and i will be using windows, kaggle

Practice questions to prepare for Generative AI LL Ms Associate (NCA-GENL)!This certification is designed to validate foundational knowledge and practical skills in working with large language models (LL Ms) and generative AI. This certification is ideal for professionals aiming to develop expertise in deploying and managing LLM-based solutions. Key focus areas include understanding transformer-based architectures, prompt engineering techniques for guiding model responses, and leveraging modern pretrained models to solve a range of natural language processing (NLP) tasks, such as text generation, token classification, and sentiment analysis. The certification covers best practices for working with human-labeled data and strategies for optimizing models for specific applications. This certification is ideal for those looking to strengthen their understanding of generative AI and advanced technologies within the rapidly evolving AI landscape.About the course Prepare yourself for success in the Generative AI LL Ms certification with this comprehensive mock exam course. This course is specifically designed to help you master the key concepts and skills needed to excel in the rapidly growing field of Generative AI, focusing on Large Language Models (LL Ms).This course features six carefully crafted mock exams that closely mirror the format, difficulty, and scope of the actual certification exam. Each mock exam contains a diverse set of questions that test your knowledge on various topics, including the fundamentals of Generative AI, architecture and deployment of LL Ms, model training and fine-tuning, ethical considerations, and specific tools and platforms for AI development.What sets this course apart is the detailed explanations provided for each question. After completing each exam, you will not only see which answers you got right or wrong but also receive in-depth explanations that clarify why certain answers are correct. This approach

This course is diving into Generative AI State-Of-Art Scientific Challenges. It helps to uncover ongoing problems and develop or customize your Own Large Models Applications. Course mainly is suitable for any candidates(students, engineers,experts) that have great motivation to Large Language Models with Todays-Ongoing Challenges as well as their&nbsp; deeployment with Python Based and Javascript Web Applications, as well as with C/C++ Programming Languages. Candidates will have deep knowledge on&nbsp; TensorFlow , PyTorch,&nbsp; Keras models, Hugging Face with Docker Service. In addition, one will be able to optimize and quantize TensorRT frameworks for deployment in variety of sectors. Moreover, They will learn deployment of LLM quantized model to Web Pages developed with React, Javascript and FLASK Here you will also learn how&nbsp; to integrate Reinforcement Learning(PPO) to Large Language Model, in order to fine them with Human Feedback based.&nbsp; Candidates will learn how to code and debug in C/C++ Programming languages at least in intermediate level.LLM Models used: The Falcon, LLAMA2, BLOOM, MPT, Vicuna,FLAN-T5, GPT2/GPT3, GPT&nbsp;NEOXBERT 101, Distil BERTFINE-Tuning Small Models under supervision of BIG Models Image Generation :LLAMA models Gemini Dall-E OpenAI Hugging Face Models Learning and Installation of Docker from scratch Knowledge of Javscript, HTML ,CSS, Bootstrap React Hook,&nbsp;DOM and Javacscript Web Development Deep Dive on

Have you ever watched AI automatically classify images or detect spam and thought, “I wish I could do that”? Have you ever wondered how a spam filter works? Or do you want to master Deep Learning in a hands-on way?&nbsp;With this course, you’ll learn how to build and deploy your own deep learning models in just 15 days - gaining practical, hands-on experience every step of the way.Why This Course?From day one, you’ll get comfortable with the essential concepts that power modern AI. No fluff, no endless theory - you'll learn by building real-world projects like Spam filters, or image detections. By the end, you won’t just know what neurons and neural networks are - you’ll be able to train, refine, and apply them to projects that truly matter.Who Is This Course For?Absolute beginners eager to break into the world of AI and deep learning.Data enthusiasts who want to strengthen their portfolios with hands-on projects.Developers and data scientists looking to deepen their PyTorch and model deployment skills.Anyone who craves a clear roadmap to mastering deep learning, one day at a time.What Makes This Course Unique?Day-by-Day Progression: Follow a structured, 15-day plan that ensures you never feel lost or overwhelmed.Real-World Projects: Predict used car prices, detect spam in SMS, classify handwritten digits, recognize fashion items—all using deep learning techniques.Modern Tools &amp; Frameworks: Master industry-standard tools like PyTorch and dive into CNNs, transfer learning with Res Net, and more.Practical Deployment: Learn how to turn your trained models into interactive apps with Gradio, making your projects truly come alive.By the End of This Course, You Will:Confid