In this course you'll explore advanced natural language processing - the field of computer science and AI that concerns interactions between computer and human languages. Over the course you'll learn four new NLP architectures and explore classic NLP problems like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. By course's end, you'll have a firm grasp on natural language processing and its many applications.
- Access 40 lectures & 4.5 hours of content 24/7
- Discover Word2Vec & how it maps words to a vector space
- Explore GLoVe's use of matrix factorization & how it contributes to recommendation systems
- Learn about recursive neural networks which will help solve the problem of negation in sentiment analysis
The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons to communicate with their family and caregivers.
He has worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. He has created new big data pipelines using Hadoop/Pig/MapReduce, and created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.
He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.
Details & Requirements
- Length of time users can access this course: lifetime
- Access options: web streaming, mobile streaming
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: advanced, but you must have some knowledge of calculus, linear algebra, probability, Python, Numpy, and be able to write a feedforward neural network in Theano and TensorFlow.
- All code for this course is available for download here, in the directory nlp_class2
- Instant digital redemption