We show you how one might code their own linear regression module in Python. Linear regression is the simplest machine learning model you can learn, y

In this course we are going to look at NLP (natural language processing) with deep learning. Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices. These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words. In this course I'm going to show you how to do even more awesome things. We'll learn not just 1, but 4 new architectures in this course.

Python Programming Basics and Python Object Oriented Programming Guide for Python Programmers & Python Coders in a simple and easy way with Examples, quizzes, Resources & Python Projects to master Python from zero to hero. Why to master Python Programming? Python is a high level programming language, strong, elegant, and easy to learn. Faster than R programming language when used for data science. Has lots of libraries which facilitate its use for data analysis.

This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.

Recent research in Natural Language Processing (NLP) has led to the release of multiple large transformer-based language models like OpenAI's GPT-[2,3], EleutherAI's GPT-[Neo, J], and Google's T5. For those not impressed by the leap of tunable parameters in the billions, the ease with which these models could perform on a never before seen task without training a single epoch is something to behold. While it has become evident that the more parameters a model has the better it will generally perform, an exception to this rule applies when one explores fine-tuning. Fine-tuning refers to the practice of further training transformer-based language models on a dataset for a specific task. This practice has led to the 6 billion parameter GPT-J outperforming the 175 billion GPT-3 Davinci on a number of specific tasks. As such, fine-tuning will continue to be the modus operandi when using language models in practice, and, consequently, fine-tuning is the main focus of this post.

These days, education is more important to businesses than ever. Not only do companies need to keep employees properly trained and certified, but employers also have to be mindful of how their remote employees are educating their children: Parents who are dissatisfied with how their kids are learning or who are even resorting to homeschooling will probably demonstrate the impact of those burdens in terms of productivity. One option that could make both of those scenarios easier is using artificial intelligence (AI) for teaching--and it's not as far-fetched as you might think. A recent study by Tidio, an AI chatbot developer for apps such as help desks, shows that 53% of its US respondents said they'd be fine with an AI teaching their kids. The study collected answers from 1,027 respondents using Amazon's Mechanical Turk and Reddit.

Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started, build accurate models and confidently complete predictive modeling machine learning projects using Python in 14 days. This is a big and important post. You might want to bookmark it. Python Machine Learning Mini-Course Photo by Dave Young, some rights reserved.

Deep learning is a subset of machine learning, which in turn is a subset of artificial intelligence, but the origins of these names arose from an interesting history. In addition, there are fascinating technical characteristics that can differentiate deep learning from other types of machine learning…essential working knowledge for anyone with ML, DL, or AI in their skillset. If you are looking to improve your skill set or steer business/research strategy in 2021, you may come across articles decrying a skills shortage in deep learning. A few years ago, you would have read the same about a shortage of professionals with machine learning skills, and just a few years before that the emphasis would have been on a shortage of data scientists skilled in "big data." Likewise, we've heard Andrew Ng telling us for years that "AI is the new electricity", and the advent of AI in business and society is constantly suggested to have an impact similar to that of the industrial revolution.

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