MLOps | Machine Learning Operations Specialization

You're here, so you've probably heard about ChatGPT and how it's going to change the world. This course is designed for those who are interested in leveraging A.I ChatGPT in their specific domain or niche. Whether you're a marketer, business strategist, finance professional, teacher or student, or a creative artist - this course goes over dozens of examples on how to get the most out of ChatGPT. Even if you're a writer, or a tradesperson - this class can still be of great use to you. The entire world will use this tool within a matter of months. Chat GPT already is changing the world, and increasing the productivity of those who can best use it. "Leverage" is the key word here, and in this course my attempt is to share as many possible forms of maximizing the output A.I can give you. Whether that's through your existing business or career, or you want to discover new and creative ways to increase your income - this course covers many things. We live in a digital information Era, and ChatGPT is a way to interact with text/data that has never existed before. ChatGPT has more signups in a month than some of the top services like Facebook had in months. In this class, I share my mental framework for thinking about how to interact with ChatGPT A.I, as well as numerous examples that include: - Multiple ideas for Leveraging A.I for income generation (A.I Arbitrage Opportunities) - E-Commerce Tactics to Speed up your workflows - Leveraging A.I for creative art direction (Filmmakers, Artists, Art Directors, Creative Directors, Book Writers) - SEO Strategies and Advertising Strategies for all online businesses - How to create contracts and emails within seconds (the right way) - How to "Compress" and "Decompress" information selectively - Using A.I to create simple snippets code, to speed up your workflows (Even if you aren't a coder!) - Using AI for software specific problems - Using AI for Writing, Using AI to correct Writing - Using A.I for students writing, and teachers needing to create content around courses - Using A.I to discover what you don't know (Extremely useful.) - Using A.I to help learn languages - Understanding Iteration, and why ChatGPT is not Google. - How to follow the latest technological trends, who to follow - and knowing where to look. If you hear things from the news, you're already late. - Mixing Chat GPT with AI Text-to-Image Art Generation, using Midjourney or Stable Diffusion - Understanding how an A.I Chatbox works versus a search engine - Using Chat GPT to create personalized fitness plans - Using Chat GPT to deliver compelling sales pitches

This IBM course will teach you how to implement, train, and evaluate generative AI models for natural language processing (NLP). The course will help you acquire knowledge of NLP applications including document classification, language modeling, language translation, and fundamentals for building small and large language models. You will learn about converting words to features. You will understand one-hot encoding, bag-of-words, embedding, and embedding bags. You also will learn how Word2Vec embedding models are used for feature representation in text data. You will implement these capabilities using PyTorch. The course will teach you how to build, train, and optimize neural networks for document categorization. In addition, you will learn about the N-gram language model and sequence-to-sequence models. This course will help you evaluate the quality of generated text using metrics, such as BLEU. You will practice what you learn using Hands-on Labs and perform tasks such as implementing document classification using torchtext in PyTorch. You will gain the skills to build and train a simple language model with a neural network to generate text and integrate pre-trained embedding models, such as word2vec, for text analysis and classification. In addition, you will apply your new skills to develop sequence-to-sequence models in PyTorch and perform tasks such as language translation.

In this course, we are going to learn about natural language processing. We will discuss various concepts such as tokenization, stemming, and lemmatization to process text. We will then discuss how to build a Bag of Words model and use it to classify text. We will see how to use machine learning to analyze the sentiment of a given sentence. We will then discuss topic modeling and implement a system to identify topics in a given document. We will start with simple problems in NLP such as Tokenization Text, Stemming, Lemmatization, Chunks, Bag of Words model. and we will build some real stuff such as : 1. Learning How to Represent the Meaning of Natural Language Text 2. Building a category predictor to predict the category of a given text document. 3. Constructing a gender identifier based on the name. 4. Building a sentiment analyzer used to determine whether a movie review is positive or negative. 5. Topic modeling using Latent Dirichlet Allocation 6. Feature Engineering 7. Dealing with corpora and WordNet 8. Dealing With your Vocabulary for any NLP and ML model TIPS (for getting through the course): - Take handwritten notes. This will drastically increase your ability to retain the information. - Ask lots of questions on the discussion board. The more the better! - Realize that most exercises will take you days or weeks to complete. - Write code yourself, don’t just sit there and look at my code. You don't know anything about NLP? let's break it down! I am always available to answer your questions and help you along your data science journey. See you in class! NOTICE that This course will be modified and I will add new content and new concepts from one time to another, so stay informed! :)