This course explores advanced machine learning techniques, neural networks, and natural language processing (NLP), all of which are critical in today’s data-driven world. Mastery of these skills enables professionals to solve complex problems in areas such as AI, automation, and big data analytics.

Advanced Machine Learning, Neural Networks, and NLP

Advanced Machine Learning, Neural Networks, and NLP
This course is part of CompTIA DataX Study Guide Specialization

Instructor: Wiley-Expert Edge Course Instructors
Access provided by SGCSRC
Gain insight into a topic and learn the fundamentals.
Advanced level
Recommended experience
5 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Master advanced supervised machine learning techniques such as regression, decision trees, and ensemble methods.
Understand neural network architectures and deep learning techniques for complex problem-solving.
Explore NLP techniques like tokenization, sentiment analysis, and word embeddings for text data processing.
Skills you'll gain
- Convolutional Neural Networks
- Data Science
- Deep Learning
- Machine Learning Algorithms
- Artificial Neural Networks
- Statistical Machine Learning
- Data Processing
- Logistic Regression
- Natural Language Processing
- Applied Machine Learning
- Text Mining
- Decision Tree Learning
- Computer Vision
- Supervised Learning
- Advanced Analytics
- Machine Learning Methods
- Model Optimization
- Image Analysis
- Artificial Intelligence and Machine Learning (AI/ML)
- Machine Learning
Details to know

Shareable certificate
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Assessments
4 assignments
Taught in English
Recently updated!
April 2026
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Build your subject-matter expertise
This course is part of the CompTIA DataX Study Guide Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate

There are 4 modules in this course
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