Machine Learning and Natural Language Processing Course

Course Summary

The Machine Learning and Natural Language Processing training course provides a foundational understanding of Machine Learning (ML) and Natural Language Processing (NLP).

This course begins by demonstrating techniques that can be used to solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. Next, practice exercises give hands-on experience in implementing ML models on real datasets. The course concludes by demonstrating how to implement ML algorithms with TensorFlow and other open source libraries used by leading tech companies in the ML field.

This course is designed for individuals with existing development experience.

Purpose

Learn to implement ML techniques for Natural Language comprehension, sentiment analysis, topic discovery, etc.

Audience

Developers and developer teams wanting to work more with NLP.

Role

Data Engineer - Data Scientist - Software Developer

Skill Level

Intermediate

Style

Fast Track - Targeted Topic - Workshops

Duration

2 Days

Related Technologies

Artificial Intelligence

Natural Language Processing

Tensorflow

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Productivity Objectives:

Examine the problems associated with natural language processing
Describe the basic techniques used in natural language processing (e.g., text classification, clustering, semantic analysis, sentiment analysis, topic discovery)
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about our training

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
Expert Practitioners

Industry experts with 15+ years of industry experience that bring their battle scars into the classroom.
Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
Fully Customized

One-size-fits-all doesn't apply to training teams. That's where we come in!

What You'll Learn

In the Machine Learning and Natural Language Processing training course, you'll learn:

Problem of Human Language
- Approaches and Techniques
- Frameworks and Tools
- RASA, NLTK, Spacy, Prediction IO
Document Term Matrices and Bag of Words Approach
- Building Matrix terms using BOW
- Word Count Representation
- TF-IDF representation
- Using rows in DTM to vectorize data
Search and Keyword Extraction
- Introducing Search
- Keyword Extraction
- N-grams
Vectorizing NL Data
- Using Word2Vec to vectorize words
- Doc2Vec for vectorizing NL Documents
- Analyzing Word2Vec representations
- GloVE as an alternative to Word2Vec
Performing Text Classification and Clustering with Vectorized Data
- Feature Representations for ML
- k-Means Clustering on Vectorized Data
- Classification of Vectorized Data
Sentiment Analysis
- Performing Sentiment Scoring
- Using Sentiment Classification Algorithms
- Using RASA library to train sentiment analysis
Semantic Analysis
- NLP Linguistics Pipelines
- Parsing / Chunking / POS Tagging
- Viewing NL as a Bayesian Network
Topic Discovery
- Introducing Topic Modeling
- DTM Rows and LSA
- LDA as a better Topic Discovery Algorithm
- Topic Discovery With Prediction.IO
Intents Classification
Sequential Modeling
- Modeling Speech as a Sequence
- Markov Models / HMMs
- Bayesian Networks
- LSTMs
- Conversation Chatbot using RASA