Advanced Machine Learning, Neural Networks, and NLP

Overview

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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.

Syllabus

Supervised Machine Learning

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.

Neural Networks and Deep Learning

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.

Natural Language Processing

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.

Specialized Applications of Data Science

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.