Advanced AI/ML Learning Path

Learn Transformers, explained: Understand the model behind GPT, BERT, and T5

State-of-the-Art Machine Learning Papers Implementation

Statistical Learning

Complete natural language processing specialization covering transformers, attention mechanisms, and modern NLP techniques.

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.Next, we implement a neural network using Google's new TensorFlow library.You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.Another project at the end of the course shows you how you can use deep learning for facial

THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH TensorFlow & KERAS IN PYTHON!It is a full 7-Hour Python TensorFlow & Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using two of the most important Deep Learning frameworks- TensorFlow and Keras. HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:This course is your complete guide to practical machine & deep learning using the TensorFlow & Keras framework in Python.. This means, this course covers the important aspects of Keras and TensorFlow (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python TensorFlow and Keras based data science. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of TensorFlow and Keras is revolutionizing Deep Learning... By gaining proficiency in Keras and and TensorFlow, you can give your company a competitive edge and boost your career to the next level.THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL KERAS & TensorFlow BASED DATA SCIENCE!But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably wit

Welcome to the Advanced Machine Learning & Deep Learning Masterclass 2024! This comprehensive course is designed for both business professionals and researchers, offering over 24 hours of in-depth video content. Whether you're new to Python programming or experienced in the field, this course equips you with essential machine learning and deep learning techniques, from foundational Python skills to advanced neural network architectures.What You Will Learn:Python for Machine Learning: Set up the environment, use popular tools like Anaconda and Py Charm, and learn Python basics through step-by-step tutorials.Data Understanding & Preprocessing: Dive deep into statistical analysis, data pre-processing techniques, feature selection, and data visualization with Python.Artificial Neural Networks: Build neural networks from scratch, explore deep learning frameworks like Keras, and implement a full deep learning project on handwritten digit recognition.Advanced Deep Learning Mastery: Go beyond the basics with comprehensive modules on Convolutional Neural Networks (CNNs), transformers, large language models, and deep generative models. You'll learn how to construct and train models that power today’s AI innovations, including reinforcement learning and sequence models.Naive Bayes Classifier & NLP: Learn the fundamentals of Naive Bayes classification and explore natural language processing, including tokenization, part-of-speech tagging, and real-world NLP projects.Linear & Logistic Regression: Master regression models with hands-on demos for univariate and multivariate scenarios.With practical hands-on demos, coding exercises, and real-world proj

Are you ready to unlock the full potential of Deep Learning and AI by mastering not just one but multiple tools and frameworks? This comprehensive course will guide you through the essentials of Deep Learning using Python, PyTorch, and TensorFlow—the most powerful libraries and frameworks for building intelligent models.Whether you're a beginner or an experienced developer, this course offers a step-by-step learning experience that combines theoretical concepts with practical hands-on coding. By the end of this journey, you'll have developed a deep understanding of neural networks, gained proficiency in applying Deep Neural Networks (DNNs) to solve real-world problems, and built expertise in cutting-edge deep learning applications like Convolutional Neural Networks (CNNs) and brain tumor detection from MRI images.Why Choose This Course?This course stands out by offering a comprehensive learning path that merges essential aspects from three leading frameworks: Python, PyTorch, and TensorFlow. With a strong emphasis on hands-on practice and real-world applications, you'll quickly advance from fundamental concepts to mastering deep learning techniques, culminating in the creation of sophisticated AI models.Key Highlights:Python: Learn Python from the basics, progressing to advanced-level programming essential for implementing deep learning algorithms.PyTorch: Master PyTorch for neural networks, including tensor operations, optimization, autograd, and CNNs for image recognition tasks.TensorFlow: Unlock TensorFlow's potential for creating robust deep learning models, utilizing tools like Tensorboard for model visualization.Real-world Projects: Apply your knowledge to exciting projects like IRIS classi

AI is omnipresent in our modern world. It is in your phone, in your laptop, in your car, in your fridge and other devices you would not dare to think of. After thousands of years of evolution, humanity has managed to create machines that can conduct specific intelligent tasks when trained properly. How? Through a process called machine learning or deep learning, by mimicking the behaviour of biological neurons through electronics and computer science. Even more than it is our present, it is our future, the key to unlocking exponential technological development and leading our societies through wonderful advancements. As amazing as it sounds, it is not off limits to you, to the contrary!We are both engineers, currently designing and marketing advanced ultra light electric vehicles. Albert is a Mechanical engineer specializing in advanced robotics and Eliott is an Aerospace Engineer specializing in advanced space systems with past projects completed in partnership with the European Space Agency. The aim of this course is to teach you how to fully, and intuitively understand neural networks, from their very fundamentals. We will start from their biological inspiration through their mathematics to go all the way to creating, training and testing your own neural network on the famous MNIST database.It is important to note that this course aims at giving you a complete and rich understanding of neural networks and AI, in order to give you the tools to create your own neural networks, whatever the project or application. We do this by taking you through the theory to then apply it on a very hands-on MATLAB project, the goal being for you to beat our own neural network's performance!This course will give you the opportunity to understand, use and create:How to emulate real brains with neural networks.How to represent and annotate neural networks.How to build and compute neural ne

Welcome to the "Deep Learning Neural Networks with TensorFlow" course! This comprehensive program is designed to equip you with the essential knowledge and hands-on skills required to navigate the exciting field of deep learning using TensorFlow.Overview: In this course, you will embark on a journey through the fundamentals and advanced concepts of deep learning neural networks. We'll start by providing you with a solid foundation, introducing the core principles of neural networks, including the scenario of Perceptron and the creation of neural networks using TensorFlow.Hands-on Projects: To enhance your learning experience, we have incorporated practical projects that allow you to apply your theoretical knowledge to real-world scenarios. The "Face Mask Detection Application" project in Section 2 and the "Implementing Linear Model with Python" project in Section 3 will provide you with valuable hands-on experience, reinforcing your understanding of TensorFlow.Advanced Applications: Our course goes beyond the basics, delving into advanced applications of deep learning. Section 4 explores the fascinating realm of automatic image captioning for social media using TensorFlow. You will learn to preprocess data, define complex models, and deploy applications, gaining practical insights into the cutting-edge capabilities of deep learning.Why TensorFlow? TensorFlow is a leading open-source deep learning framework, widely adopted for its flexibility, scalability, and extensive community support. Whether you're a beginner or an experienced professional, this course caters to learners of all levels, guiding you through the intricacies of deep learning with TensorFlow.Get ready to unravel the mysteries of neural networks, develop practical skills, and unleash the power of TensorFlow in the dynamic field of deep learning. Join us on this exciting learning journey, and let's dive deep into the

THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH PyTorch, H2O, KERAS & TensorFlow IN PYTHON!It is a full 5-Hour+ Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using one of the most important Python Deep Learning frameworks- PyTorch, H2O, Keras & TensorFlow.                         HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:This course is your complete guide to practical machine & deep learning using the PyTorch, H2O, Keras and TensorFlow framework in Python. This means, this course covers the important aspects of these architectures and if you take this course, you can do away with taking other courses or buying books on the different Python-based- deep learning architectures.  In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of frameworks such as PyTorch, Keras, H2o, TensorFlow is revolutionizing Deep Learning... By gaining proficiency in PyTorch, H2O, Keras and TensorFlow, you can give your company a competitive edge and boost your career to the next level.THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON BASED DATA SCIENCE!But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment), graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real-life data from different sources using data science-related techniques and producing publications for international peer-reviewed journals.&nbsp

This course will teach you Deep learning focusing on Convolution Neural Net architectures. It is structured to help you genuinely learn Deep Learning by starting from the basics until advanced concepts. We will begin learning what it is under the hood of Deep learning frameworks like TensorFlow and PyTorch, then move to advanced Deep learning Architecture with PyTorch.During our journey, we will also have projects exploring some critical concepts of Deep learning and computer vision, such as: what is an image; what are convolutions; how to implement a vanilla neural network; how back-propagation works; how to use transfer learning and more.All examples are written in Python and Jupyter notebooks with tons of comments to help you to follow the implementation. Even if you don’t know Python well, you will be able to follow the code and learn from the examples.The advanced part of this project will require GPU but don’t worry because those examples are ready to run on Google Colab with just one click, no setup required, and it is free! You will only need to have a Google account. By following this course until the end, you will get insights, and you will feel empowered to leverage all recent innovations in the Deep Learning field to improve the experience of your projects.

In this course you will Machine Learning And Neural Networks easily. We will develop Keras / TensorFlow Deep Learning Models using GUI and without knowing Python or programming.If you are a python programmer, in this course you will learn a much easier and faster way to develop and deploy Keras / TensorFlow machine learning models.You will learn about important machine learning concepts such as datasets, test set splitting, deep neural networks, normailzation, dropout, artificial networks, neural network models, hyperparameters, WITHOUT hard and boring technical explanations or math formulas, or follow along code. Instead, you will learn these concepts from practical and easy to follow along teaching methods. In this course, Deep Learning Studio will produce all the python code for you in the backend, and you never even have to even look at it (unless of course you want to). By the end of this course you will be able to build, train and deploy DeepLearning.AI models without having to do any coding.After taking this course you will be able to produce well written professional python code without even knowing what python is or how to program, Deep Learning Studio will do all this work for you. Instead you can easily stay focused on building amazing artificial intelligence machine learning solutions without programming.Also, if you just want to learn more about Deep Learning Studio and get a jump start on this revolutionary ststem, this is the course for you! Deep Learning Studio is just beginning to shake up the data science world and how artificial intelligence solutions are developed! Get ahead of the curve by taking this exciting and easy to follow along course!

Hi this is Abhilash Nelson and I am thrilled to introduce you to my new course Deep Learning and Neural Networks using Python: For Dummies The world has been revolving much around the terms "Machine Learning" and "Deep Learning" recently. With or without our knowledge every day we are using these technologies. Ranging from google suggestions, translations, ads, movie recommendations, friend suggestions, sales and customer experience so on and so forth. There are tons of other applications too. No wonder why "Deep Learning" and "Machine Learning along with Data Science" are the most sought after talent in the technology world now a days.But the problem is that, when you think about learning these technologies, a misconception that lots of maths, statistics, complex algorithms and formulas needs to be studied prior to that. Its just like someone tries to make you believe that, you should learn the working of an Internal Combustion engine before you learn how to drive a car. The fact is that, to drive a car, we just only need to know how to use the user friendly control pedals extending from engine like clutch, brake, accelerator, steering wheel etc. And with a bit of experience, you can easily drive a car. The basic know how about the internal working of the engine is of course an added advantage while driving a car, but its not mandatory. Just like that, in our deep learning course, we have a perfect balance between learning the basic concepts along the implementation of the built in Deep Learning Classes and functions from the Keras Library using the Python Programming Language. These classes, functions and AP Is are just like the control pedals from the car engine, which we can use easily to build an efficient deep learning model.Lets now see how this course is organized and an overview about the list of topics included.We will be starting with few theory sessions in which we will see an overview about the Deep Learning an

A warm welcome to the Deep Learning for AI: Build, Train & Deploy Neural Networks course by Uplatz.Deep learning is a specialized branch of machine learning that focuses on using multi-layered artificial neural networks to automatically learn complex patterns and representations from data. Deep learning enables computers to learn and make intelligent decisions by automatically discovering the representations needed for tasks such as classification, prediction, and more—all by processing data through layers of artificial neurons.Deep learning is a subfield of machine learning that focuses on using artificial neural networks with many layers (hence “deep”) to learn complex patterns directly from data. It has revolutionized how we approach problems in image recognition, natural language processing, speech recognition, and more. Below is an overview covering how deep learning works, its key features, the tools and technologies used, its benefits, and the career opportunities it presents.Some of its key features are:Neural Networks at its Core Deep learning models are built on neural networks that consist of multiple layers (hence "deep") of interconnected nodes or neurons. These layers process input data step-by-step, each extracting increasingly abstract features.Learning Hierarchies of Features The initial layers might capture simple patterns (like edges in an image), while deeper layers build on these to recognize more complex patterns (like shapes or even specific objects).Automatic Feature Extraction Unlike traditional machine learning, where features are manually engineered, deep learning models learn to extract and combine features directly from raw data, which is particularly useful when dealing with large and unstructured datasets.Applications This approach is highly effecti