In this course, you will learn NLP (natural language processing) with deep learning. This course will teach you word2vec and how to implement word2vec. You will also learn how to implement GloVe using gradient descent and alternating least squares. This course uses recurrent neural networks for named entity recognition. Along with that, you will learn how to implement recursive neural tensor networks for sentiment analysis. Let's see the topics covered in this course-

Welcome to the Complete Data Science and Machine Learning Bootcamp, the only course you need to learn Python and get into data science. Will this course give you core python skills? There is a range of exciting opportunities for Python developers. All of them require a solid understanding of Python, and that's what you will learn in this course. And this course is designed to give you those core skills, fast.

How often do you test ML models in a Jupyter notebook, get good results, but still cannot convince your boss that the model should be used right away? Or maybe you manage to convince her and put the model in production, but you do not see any impact on business metrics? Luckily for you, there are better ways to test ML models in the real world and to convince everyone (including you) that they add value to the business. In this article you will learn what these evaluation methods are, how to implement them, and when should you use each. We, data scientists and ML engineers, develop and test ML models in our local development environment, for example, a Jupyter notebook.

Welcome back to yet another interesting improvement of the Transformer (Attention is All You Need) architecture -- Compressive Transformers. This particular architecture has a lower memory requirement than Vanilla Transformer and is similar to the Transformer-XL that models longer sequences efficiently. The below image depicts how the memory is compressed. We can also say that this is drawing some parallels to the human brain -- We have a brilliant memory because of the power of compressing and storing information very intelligently. This sure seems interesting, doesn't it?

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done.

Let us start with an easy example, say you are teaching a kid to differentiate dogs from cats. How would you do it? You may show him/her a dog and say "here is a dog" and when you encounter a cat you would point it out as a cat. When you show the kid enough dogs and cats, he may learn to differentiate between them. If he is trained well, he may be able to recognise different breeds of dogs which he hasn't even seen. Similarly, in Supervised Learning, we have two sets of variables.

This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; neural networks and deep learning; survival models; multiple testing. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical). This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data science.

How is AI Being Used to Change Higher Education? Medical, financial, energy, and commerce industries are being revolutionized rapidly by artificial intelligence (AI). The use of AI technologies in Higher Education is particularly promising. In the coming years, artificial intelligence could have a huge impact on higher education. A new generation of innovations, such as virtual reality and other innovations, may be able to improve learning as well as lower costs for Generation Z and beyond. We will discuss in depth in this article how artificial intelligence can be used to make higher education a better experience for students and teachers alike. Also Read: How Technology Has Changed Teaching and Learning. It is clear why American universities are reliant on algorithms for selection models to manage enrollment by understanding the status of higher education as a whole.

Welcome to the Python for Data Science - NumPy, Pandas & Scikit-Learn course, where you can test your Python programming skills in data science, specifically in NumPy, Pandas and Scikit-Learn. This course is designed for people who have basic knowledge in Python, NumPy, Pandas and Scikit-Learn packages. It consists of 330 exercises with solutions. This is a great test for people who are learning the Python language and data science and are looking for new challenges. Exercises are also a good test before the interview. Many popular topics were covered in this course.

Editor's note: For the full scope of Data Science Books included in this 2 part series, please see The Complete Collection of Data Science Books – Part 1. The data science books have been an influential part of my data science journey. The Deep Learning for Coders with Fastai and PyTorch has made me think outside the box about deep neural networks and how we approach almost any machine learning issue. I am in love with NLP books and how they come with GitHub repositories, Jupyter notebooks exercise, and easy to explore options. Data Science at the Command Line is one of the books that are now available online (documentation style) with the ability to search terms, navigation, and copy the code directly to test the example.