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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 Skills Network

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

4 modules

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.

Details to know

Shareable certificate

Add to your LinkedIn profile

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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

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.

By diving deep into supervised learning, neural networks, and NLP, learners will enhance their ability to create sophisticated models and systems capable of handling large-scale, unstructured data. These skills are highly valued in industries like finance, healthcare, and technology. What makes this course unique is its balance between theoretical knowledge and practical, hands-on application. You will not only grasp the underlying algorithms but also learn how to implement them in real-world projects, enhancing your ability to apply machine learning and NLP to solve real challenges. This course is ideal for data scientists, machine learning engineers, and AI researchers looking to expand their expertise. A background in basic machine learning concepts and programming is recommended for the best experience. This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization. From CompTIA DataX Study Guide Copyright © 2024 by John Wiley & Sons, Inc. All rights, including for text and data mining, AI training, and similar technologies, are reserved. Used by arrangement with John Wiley & Sons, Inc.

Module details

In this section, we examine key supervised machine learning techniques including linear and logistic regression, decision trees, and ensemble methods, while highlighting model assumptions, regularization, and real-world data science applications.

What's included

1 video7 readings1 assignment

In this section, we examine the architecture and core components of artificial neural networks, review deep learning techniques like dropout and batch normalization, and distinguish major deep learning architectures with practical use cases.

What's included

1 video6 readings1 assignment

1 videoTotal 1 minute

Neural Networks and Deep Learning - Overview Video1 minute

6 readingsTotal 65 minutes

Introduction10 minutes
Threshold Activation Function15 minutes
Batch Normalization10 minutes
One-Shot, Zero-Shot, and Few-Shot Learning10 minutes
Generative Adversarial Networks10 minutes
Exam Essentials10 minutes

1 assignmentTotal 10 minutes

Neural Networks and Deep Learning Fundamentals10 minutes

In this section, we explore key NLP techniques, covering text preparation (tokenization, stemming), text analysis (keyword extraction, sentiment analysis), and text representation (vector space models, word embeddings) for practical language processing applications.

What's included

1 video6 readings1 assignment

In this section, we compare optimization models by exploring decision variables and constraints, and explain computer vision concepts with hands-on steps for image preprocessing and feature extraction in practical applications.