In this course, you’ll see how you can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label. You could imagine that such a task is a basic prerequisite for self-driving vehicles. With 75 lectures, you’ll be looking at SSD, neural style transfer, and facial recognition. Sign up and learn these advanced applications of CNNs.
- Access 75 lectures & 7 hours of content 24/7
- See how a CNN can be turned into an object detection system
- Learn about a state-of-the-art algorithm called SSD
- Understand the process of neural style transfer
- Become informed & aware about facial recognition
You’ve learned about some of the fundamental building blocks of Deep NLP such as RNNs, CNNs, and word embedding algorithms such as word2vec and GloVe. With 65 lectures, this course will take you to a higher system level of thinking. Since you know how these things work, it’s time to build systems using these components. At the end of this course, you’ll be able to build applications for problems like text classification, neural machine translation, stock prediction.
- Access 65 lectures & 8 hours of content 24/7
- Visualize what’s happening in a machine learning model internally
- Take a look at some advanced Deep NLP techniques: bidirectional RNNs, seq2seq & attention
- Build applications for problems like text classification, neural machine translation & stock prediction
Believe it or not, almost all online businesses today make use of recommender systems in some way or another. Recommender systems form the very foundation of the internet’s top three websites. Google uses Search Results, YouTube uses Video Dashboard, and Facebook uses Newsfeed. This 10-hour course is a big bag of tricks that make recommender systems work across multiple platforms. You’ll be looking at popular news feed algorithms, like Reddit, Hacker News, and Google PageRank, as well as Bayesian recommendation techniques that are being used by a large number of media companies today.
- Access 75 lectures & 10 hours of content 24/7
- Understand state-of-the-art algorithms like matrix factorization & deep learning
- Learn a bag full of tricks to improve baseline results
- Learn how techniques from natural language processing (NLP) have been used in recommenders
- Perform matrix factorization using big data in Spark
A subset of machine learning, deep learning focuses on how machines use neural networks to learn from data. These neural networks are used to perform tasks and are adjusted to create a better outcome each time, paving the way for groundbreaking machines that learn on their own! This master class takes you through machine learning, neural networks, and several core tools, like Keras, TensorFlow, and Python, as you work toward creating a model that can classify images.
- Access 44 lectures & 6 hours of content 24/7
- Walk through the essentials for using Python, Keras, TensorFlow & more machine learning tools
- Expand your understanding of machine learning, neural networks & convolutions
- Dive into creating your own image classifier model from scratch
Every company on the face of the earth wants to know what its customers feel about its products and services — and sentiment analysis is the easiest way and most accurate way of finding out the answer to this question. This course will make doing sentiment analysis really easy. It will cover 60 line sentiment analysis engines, basic machine learning with minimal math, real-life applications, and mistakes to avoid. By learning to do sentiment analysis, you would be making yourself invaluable to any company, especially those which are interested in quality assurance of their products and those working with business intelligence.
- Access 23 lectures & 2 hours of content 24/7
- Understand how to write industry-grade sentiment analysis engines w/ very little effort
- Learn the basics of machine learning w/ minimal math
- Understand not only the theoretical & academic aspects of sentiment analysis but also how to use it in your own field
- Get tips on avoiding mistakes made by new-comers to the field & the best practices to get you to your goal w/ minimal effort
Have you ever wondered why deep learning is taking over the world? Why it has revolutionized so many fields in research and industry? In this course, you will get answers to these questions through a real-world use case. This course aims to achieve the best of both worlds: it will show you why machine learning and deep learning is powerful but will not focus on theory. It will focus on practicals and deal with issues that newcomers to this field face. There is very little theory and that only when absolutely necessary.
- Access 29 lectures & 3 hours of content 24/7
- Understand machine learning & deep learning from a practical viewpoint
- Know all the problems that you might face & learn how to avoid them
- Cover the basic models of Keras as well as the advanced models that few people have an understanding of
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