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Natural Language Processing: Crash Course AI 7

Are you looking to master Data Science,Machine Learning (ML), Deep Learning(DL) and Natural Language Processing (NLP) from the ground up? This comprehensive course is designed to take you on a journey from understanding the basics to mastering advanced concepts, all while providing practical insights and hands-on experience.What You'll Learn:Foundational Concepts: Start with the basics of ML and NLP, including algorithms, models, and techniques used in these fields. Understand the core principles that drive machine learning and natural language processing.Advanced Topics: Dive deeper into advanced topics such as deep learning, reinforcement learning, and transformer models. Learn how to apply these concepts to build more complex and powerful models.Practical Applications: Gain practical experience by working on real-world projects and case studies. Apply your knowledge to solve problems in various domains, including healthcare, finance, and e-commerce.Mathematical Foundations: Develop a strong mathematical foundation by learning the math behind ML and NLP algorithms. Understand concepts such as linear algebra, calculus, and probability theory.Industry-standard Tools: Familiarize yourself with industry-standard tools and libraries used in ML and NLP, including TensorFlow, PyTorch, and Scikit-Learn. Learn how to use these tools to build and deploy models.Optimization Techniques: Learn how to optimize ML and NLP models for better performance and efficiency. Understand techniques such as hyperparameter tuning, model selection, and model evaluation.Who Is This Course For:This course is suitable for anyone interested in learning machine learning and natural language processing, from beginners to advanced learners. Whether you're a student, a professional look

More and more evidence has demonstrated that graph representation learning especially graph neural networks (GN Ns) has tremendously facilitated computational tasks on graphs including both node-focused and graph-focused tasks. The revolutionary advances brought by GN Ns have also immensely contributed to the depth and breadth of the adoption of graph representation learning in real-world applications. For the classical application domains of graph representation learning such as recommender systems and social network analysis, GN Ns result in state-of-the-art performance and bring them into new frontiers. Meanwhile, new application domains of GN Ns have been continuously emerging such as combinational optimization, physics, and healthcare. These wide applications of GN Ns enable diverse contributions and perspectives from disparate disciplines and make this research field truly interdisciplinary.In this course, I will start by talking about basic graph data representation and concepts like node data, edge types, adjacency matrix and Laplacian matrix etc. Next, we will talk about broad kinds of graph learning tasks and discuss basic operations needed in a GNN: filtering and pooling. Further, we will discuss details of different types of graph filtering (i.e., neighborhood aggregation) methods. These include graph convolutional networks, graph attention networks, confidence GC Ns, Syntactic GC Ns and the general message passing neural network framework. Next, we will talk about three main types of graph pooling methods: Topology based pooling, Global pooling and Hierarchical pooling. Within each of these three types of graph pooling methods, we will discuss popular methods. For example, in topology pooling we will talk about Normalized Cut and Graclus mainly. In Global pooling, we will talk about Set2Set and Sort Pool. In Hierarchical pooling, we will talk about diff Pool, g Pool and SAG Pool. Next, we will talk about three unsupervised graph neural network architectures: Graph

Dive into the cutting-edge world of Direct Preference Optimization (DPO) and Large Language Model Alignment with this comprehensive course designed to equip you with the skills to leverage the LLaMA3 8-billion parameter model and Hugging Face's Transformer Reinforcement Learning (TRL). Using the powerful Google Colab platform, you will get hands-on experience with real-world applications, starting with the Intel Orca DPO dataset and incorporating advanced techniques like Low-Rank Adaptation (LoRA).Throughout this course, you will:Learn to set up and utilize the LLaMA3 model within Google Colab, ensuring a smooth and efficient workflow.Explore the capabilities of Hugging Face’s TRL framework to conduct sophisticated DPO tasks, enhancing your understanding of how language models can be fine-tuned to optimize for specific user preferences.Implement Low-Rank Adaptation (LoRA) to modify pre-trained models efficiently, allowing for quick adaptations without the need to retrain the entire model, a crucial skill for real-world applications.Train on the Intel Orca DPO dataset to understand the intricacies of preference data and how to manipulate models to align with these insights.Extend your learning by applying these techniques to your own datasets. This flexibility allows you to explore various sectors and data types, making your expertise applicable across multiple industries.Master state-of-the-art techniques that prepare you for advancements in AI and machine learning, ensuring you stay ahead in the field.This course is perfect for data scientists, AI researchers, and anyone keen on harnessing the power of large language models for preference-based machine learning tasks. Whether you're looking to improve product recommendations, customize user experiences, or drive decision-making processes, the skills you acquire here will be invaluable.Join us to transform your theoretical knowledge int

Caution before taking this course:This course does not make you expert in R programming rather it will teach you concepts which will be more than enough to be used in machine learning and natural language processing models.About the course:In this practical, hands-on course you’ll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.Our main objective is to give you the education not just to understand the ins and outs of the R programming language, but also to learn exactly how to become a professional Data Scientist with R and land your first job.This course covers following topics:1. R programming concepts: variables, data structures: vector, matrix, list, data frames/ loops/ functions/ dplyr package/ apply() functions2. Web scraping: How to scrape titles, link and store to the data structures3. NLP technologies: Bag of Word model, Term Frequency model, Inverse Document Frequency model4. Sentimental Analysis: Bing and NRC lexicon5. Text mining By the end of the course you’ll be in a journey to become Data Scientist with R and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.

Mastering LoRA Fine-Tuning on Llama 1.1B with the Guanaco Chat Dataset: Training on Consumer GP Us Unleash the potential of Low-Rank Adaptation (LoRA) for efficient AI model fine-tuning with our groundbreaking Udemy course. Designed for forward-thinking data scientists, machine learning engineers, and software engineers, this course guides you through the process of LoRA fine-tuning applied to the cutting-edge Llama 1.1B model, utilizing the diverse Guanaco chat dataset. LoRA’s revolutionary approach enables the customization of large language models on consumer-grade GP Us, democratizing access to advanced AI technology by optimizing memory usage and computational efficiency.Dive deep into the practical application of LoRA fine-tuning within the Hugging Face Transformers framework, leveraging its Parameter-Efficient Fine-Tuning Library alongside the intuitive Hugging Face Trainer. This combination not only streamlines the fine-tuning process, but also significantly enhances learning efficiency and model performance on datasets.What You Will Learn:Introduction to LoRA Fine-Tuning: Grasp the fundamentals of Low-Rank Adaptation and its pivotal role in advancing AI model personalization and efficiency.Hands-On with Llama 1.1B and Guanaco Chat Dataset: Experience direct interaction with the Llama 1.1B model and Guanaco chat dataset, preparing you for real-world application of LoRA fine-tuning.Efficient Training on Consumer GP Us: Explore the transformational capability of LoRA to fine-tune large language models on consumer hardware, emphasizing its low memory footprint and computational advantages.Integration with Hugging Face Transformers: Master the use of the Hugging Face Parameter-Efficient Fine-Tuning Library and the Hugging Face Trainer for streamlined and effective model adaptation.Insightful Analysis of the L

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.In this course you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so it doesn't contain any hard math - just straight up coding in Python. All the materials for this course are FREE.After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we'll build is a cipher decryption algorithm. These have applications in warfare and espionage. We will learn how to build and apply several useful NLP tools in this section, namely, character-level language models (using the Markov principle), and genetic algorithms.The second project, where we begin to use more traditional "machine learning", is to build a spam detector. You likely get very little spam these days, compared to say, the early 2000s, because of systems like these.Next we'll build a model for sentiment analysis in Python. This is something that allows us to assign a score to a block of text that tells us how positive or negative it is. People have used sentiment analysis on Twitter to predict the stock market.We'll go over some practical tools and techniques like the NLTK (natural language toolkit) library and latent semantic analysis or LSA.Finally, we end the course by building an article spinner. This is a very hard problem and even the most popular products out there these days don't get it right. These lectures are designed to just get you started and to giv

In this course I will cover, how to develop a Sentiment Analysis model to categorize a tweet as Positive or Negative using NLP techniques and Machine Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model and finally deploying the same on Cloud platforms to let your customers interact with your model via an user interface.This course will walk you through the initial data exploration and understanding, data analysis, data pre-processing, data preparation, model building, evaluation and deployment techniques. We will explore NLP concepts and then use multiple ML algorithms to create our model and finally focus into one which performs the best on the given dataset.At the end we will learn to create an User Interface to interact with our created model and finally deploy the same on Cloud.I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.Task 1  :  Installing Packages.Task 2  :  Importing Libraries.Task 3  :  Loading the data from source.Task 4  :  Understanding the data Task 5  :  Preparing the data for pre-processing Task 6  :  Pre-processing steps overview Task 7  :  Custom Pre-processing functions Task 8  :  About POS tagging and Lemmatization Task 9  :  POS tagging and lemmatization in action.Task 10 :  Creating a word cloud of positive and negative tweets.Task 11 :  Identifying the most frequent set of words in the dataset for positive and negative cases.Task 12 :  Train Test Split Task 13 :  About TF-IDF Vectorizer Task 14 :  TF-IDF Vectorizer in action Task 15 :  About Confusion Matrix Task 16 :  About Classification Report Task 17 :  About AUC-ROCT