Advanced Machine Learning, Neural Networks, and NLP
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Advanced Machine Learning, Neural Networks, and NLP
This course is part of CompTIA DataX Study Guide Specialization
Instructor: Wiley Skills Network
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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
- Image Analysis
- Machine Learning Algorithms
- Deep Learning
- Computer Vision
- Model Optimization
- Statistical Machine Learning
- Machine Learning
- Data Science
- Natural Language Processing
- Text Mining
- Data Processing
- Decision Tree Learning
- Artificial Neural Networks
- Machine Learning Methods
- Artificial Intelligence and Machine Learning (AI/ML)
- Supervised Learning
- Applied Machine Learning
- Logistic Regression
- Advanced Analytics
Details to know
April 2026
4 assignments
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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 todayβs 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 Β© 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.
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
1 videoβ’Total 1 minute
- Supervised Machine Learning - Overview Videoβ’1 minute
7 readingsβ’Total 75 minutes
- Introductionβ’10 minutes
- Regularizationβ’15 minutes
- Logistic Regressionβ’10 minutes
- Linear Discriminant Analysisβ’10 minutes
- Decision Nodesβ’10 minutes
- Baggingβ’10 minutes
- Exam Essentialsβ’10 minutes
1 assignmentβ’Total 10 minutes
- Supervised Machine Learning Fundamentalsβ’10 minutes
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 videoβ’Total 1 minute
- Neural Networks and Deep Learning - Overview Videoβ’1 minute
6 readingsβ’Total 65 minutes
- Introductionβ’10 minutes
- Threshold Activation Functionβ’15 minutes
- Batch Normalizationβ’10 minutes
- One-Shot, Zero-Shot, and Few-Shot Learningβ’10 minutes
- Generative Adversarial Networksβ’10 minutes
- Exam Essentialsβ’10 minutes
1 assignmentβ’Total 10 minutes
- Neural Networks and Deep Learning Fundamentalsβ’10 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
1 videoβ’Total 1 minute
- Natural Language Processing - Overview Videoβ’1 minute
6 readingsβ’Total 65 minutes
- Introductionβ’10 minutes
- Language Understandingβ’10 minutes
- Stemmingβ’10 minutes
- Data Augmentation Augmentersβ’10 minutes
- Float-Valued Weighted Vectorβ’15 minutes
- GloVeβ’10 minutes
1 assignmentβ’Total 10 minutes
- Fundamentals of Natural Language Processingβ’10 minutes
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.
What's included
1 video6 readings1 assignment
1 videoβ’Total 1 minute
- Specialized Applications of Data Science - Overview Videoβ’1 minute
6 readingsβ’Total 65 minutes
- Introductionβ’10 minutes
- Constraintsβ’15 minutes
- Real World Scenarioβ’10 minutes
- Computer Visionβ’10 minutes
- Noise Reductionβ’10 minutes
- Texturesβ’10 minutes
1 assignmentβ’Total 10 minutes
- Exploring Advanced Data Science Applicationsβ’10 minutes
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