Skip to content

Contact sales

By filling out this form and clicking submit, you acknowledge our privacy policy.

Machine Learning and Natural Language Processing

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

 

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)
  • Create a chatbot

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
real world content

Real-World Content

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

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
tailored outlines

Tailored Outlines

One-size-fits-all doesn't apply to training teams. That's where we come in!
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”

VMware

Dive in and learn more

When transforming your workforce, it's important to have expert advice and tailored solutions. We can help. Tell us your unique needs and we'll explore ways to address them.

Let's chat

By filling out this form and clicking submit, you acknowledge our privacy policy.