AI Intermediate: Machine Learning Internals and Basic Natural Language Processing
Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. This training session provides a deep dive into machine learning, data mining, and statistical pattern recognition.
The demonstrations will contain:
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Supervised learning (parametric/non-parametric algorithms, support vector machines,
kernels). -
Unsupervised learning (clustering, dimensionality reduction, recommender systems).
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Deep dive into ARIMA based models for Time-series data.
The final part will completely focus on Natural Language-based
case studies and the models used for that.
First Case study is Parts of Speech tagging and the second one being Recognizing Spoken words using probability-based Hidden Markov Model.
Key skills covered:
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Measuring and Tuning performance of ML algorithms
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Most effective machine learning techniques
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Use tools like Scikit for ML tasks
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Best practices in innovation as it pertains to machine learning and AI
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You'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems
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You will learn how to Prototype and then productionize
Who should attend:
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Data Scientist
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People who want to take their skills to the next level especially to State-of-the-art NLP
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Software Engineers
Key skills:
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Solid foundation of some of the unsupervised learning Algorithms( PCA over covariance, PCA over SVD, Clustering(Kmean, Hierarchical, DBSCAN))
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Solid foundation of probabilistic Techniques especially Hidden Markov models and their use in Natural Language Processing(NLP)
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Solid foundation of some of the supervised learning Algorithms( ARIMA, Random Forest, Descision Trees)
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Solid foundation of the basic Engineering that goes behind Machine Learning.
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An idea of what State-of-the-art Artificial Intelligence can achieve
Pre-requisites:
Introduction to AI: Machine Learning Basics