Sentiment Analysis (or) Opinion Mining is a field of NLP that deals with extracting subjective information (positive/negative, like/dislike, emotions). Learn why it's useful and how to approach the problem. There are Rule-Based and ML-Based approaches. The details are really important - training data and feature extraction are critical. Sentiment Lexicons provide us with lists of words in different sentiment categories that we can use for building our feature set.

Machine learning is quite the buzzword these days. While it's been around for a long time, today its applications are wide and far-reaching - from computer science to social science, quant trading and even genetics. From the outside, it seems like a very abstract science that is heavy on the math and tough to visualize. But it is not at all rocket science. Machine learning is like any other science - if you approach it from first principles and visualize what is happening, you will find that it is not that hard.

The things that Mike taught are practical and can be applied in the real world immediately." This is the very FIRST course in a series of courses that will focus on NLTK. Natural Language ToolKit (NLTK) is a comprehensive Python library for natural language processing and text analytics. Note: This isn't a modeling building course. This course is laser focused on a very specific part of natural language processing called tokenization.

Variational autoencoders and GANs have been 2 of the most interesting developments in deep learning and machine learning recently. Yann LeCun, a deep learning pioneer, has said that the most important development in recent years has been adversarial training, referring to GANs. GAN stands for generative adversarial network, where 2 neural networks compete with each other. Unsupervised learning means we're not trying to map input data to targets, we're just trying to learn the structure of that input data. Once we've learned that structure, we can do some pretty cool things.

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Building apps for cloud is a thing of the past. Yes, the new wave is Artificial Intelligence (AI) and machine learning. Unfortunately, AI apps that happen to learn from the experiences are lagging way behind when compared to self-driving cars and their evolvement. Since the whole concept of AI revolves around making life easier for the user, the same rule of thumb applies to artificial intelligence apps. Developers are perplexed about what exactly goes into developing such apps. An AI-powered robot was recently granted citizenship, which is a neon sign that artificial intelligence is certainly the next big thing with most of the tech giants footing in huge money into its research and development.

We will be using the Anaconda distribution of Python throughout this course. Using the Anaconda Prompt (you can search for this program after Anaconda has installed), type conda install jupyter to install Jupyter. Jupyter is a notebook style interface for interactive coding. To launch Jupyter, open your Anaconda Prompt and type jupyter notebook. This will launch a new notebook instance in your internet browser.

Computer vision is one of today's most exciting application fields of Machine Learning, From self-driving cars to Medical diagnosis, this has been widely used in various domains. This course will take you right from the essential concepts of statistical learning to help you with various algorithms to implement it with other OpenCV tasks. The course will also guide you through creating custom graphs and visualizations, and show you how to go from the raw data to beautiful visualizations. We will also build a machine learning system that can make a medical diagnosis. By the end of this course, you will be ready create your own ML system and will also be able to take on your own machine learning problems.

See you in the class! Please note that some of our lectures are marked with (COMING SOON) - as we are still adding new, interesting videos.