Machine Learning Explained

Rated 4.95 out of 5 based on 300 customer ratings

$27

ML Fundamentals in-depth
Key Concepts, Algorithms, Data Processing
Applications Across Industries
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About Machine Learning Explained

Introducing our comprehensive guide to Machine Learning for beginners. With this ebook, you will gain a deep understanding of the key concepts and techniques used in modern machine learning.

From supervised and unsupervised learning to deep learning and neural networks, this book covers all the fundamental principles of machine learning. You will also learn about the various algorithms and techniques used in machine learning, and how to evaluate and improve the performance of machine learning models.

This is a limited-time offer!

Introduction to machine learning
What is machine learning and how does it differ from other forms of AI?
1. Other forms of AI
2. Key differences between machine learning and AI
Common applications of machine learning
The basics of machine learning algorithms
Types of data in machine learning
Preparing and preprocessing data for machine learning
Supervised learning algorithms
Unsupervised learning algorithms
Evaluating and improving the performance of machine learning models
Challenges and limitations of machine learning
1. Data quality and quantity
2. Overfitting and underfitting
3. Choosing the right algorithm and hyperparameters
4. Human bias
5. Explainability and interpretability
6. Computational resources
7. Ethical considerations
8. Robustness and generalization
9. Security and privacy
10. Human oversight
11. Ethical considerations
Neural networks and deep learning
Artificial intelligence vs. Machine learning vs. Deep learning
Natural language processing with machine learning
1. Several approaches to NLP with machine learning
Computer vision and image recognition with machine learning
Predictive analytics with machine learning
An introduction to reinforcement learning
Real-world applications of machine learning in various industries
1. Healthcare
2. Finance
3. Retail
4. Manufacturing
5. Transportation
6. Energy
7. Education
8. Cybersecurity
9. Advertising
10. Social media
11. Supply chain management
12. Customer service
13. HR
14. Telecommunications
The Role of Machine Learning in Transforming Industries and Professions
The ethics and social implications of machine learning
1. Bias in data
2. Transparency
3. Privacy
4. Autonomy
5. Social and economic impacts
6. Responsibility and accountability
Machine learning and big data
The role of data visualization in machine learning
Ensemble methods in machine learning
Transfer learning in machine learning
Role of machine learning in shaping society
Integration of machine learning with other technologies
Ethical implications of advanced machine learning systems
1. Bias in machine learning algorithms
2. Uninterpretability of machine learning algorithm
3. Privacy concerns
4. Potential for misuse or abuse of machine learning
5. Need for regulation
Future of machine learning, where we’re heading?
Conclusion

‘…frontrunner in delivering top-tier digital business resources…’

‘…has been making waves with its flagship product…’

‘…consistent emphasis on simplifying complex business topics…’

‘…shaping the future of digital business learning…’

‘…valuable resource in the ever-evolving world of business education…’

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Reviews (300)

300 reviews for Machine Learning Explained

Rated 5 out of 5

Thomas Harris Verified Buyer – 20/02/2022

I used to think ML was only for expert programmers, but this book showed me that anyone can start learning it with the right guidance.
Rated 5 out of 5

Chloe Perez Verified Buyer – 15/02/2022

If you’re looking for a book that makes Machine Learning less intimidating, this is it. ML feels so much more “approachable” after reading this!
Rated 4 out of 5

Jack Moore Verified Buyer – 08/02/2022

The book does a solid job covering ML basics and key techniques. I liked the breakdown of algorithms, but I wish it included more hands-on exercises to reinforce learning.
Rated 5 out of 5

Scarlett Gonzalez Verified Buyer – 06/02/2022

I’ve tried reading ML materials before, but they were full of jargon. This book, on the other hand, translates ML into plain, understandable language.
Rated 5 out of 5

Sebastian Walker Verified Buyer – 21/01/2022

Lots of real-world examples make concepts easier to grasp. The way it explains the difference between Supervised and Unsupervised Learning is especially helpful.
Rated 5 out of 5

Harper Carter Verified Buyer – 20/01/2022

The section on AI and Deep Learning really helped me connect the dots. Neural networks aren’t as scary as I thought!
Rated 5 out of 5

Elijah Rodriguez Verified Buyer – 19/01/2022

I was worried I wouldn’t be able to keep up since I have no coding background, but the explanations in this book are super smooth. Perfect for beginners!
Rated 5 out of 5

Charlotte Thompson Verified Buyer – 19/01/2022

I love how the author writes—not dry or overly technical, just very approachable. It’s easy to follow without feeling overwhelmed.
Rated 5 out of 5

Benjamin Miller Verified Buyer – 14/01/2022

This book feels like a guide leading you step by step from the basics to more advanced topics. I feel way more confident after reading it!
Rated 5 out of 5

Grace Anderson Verified Buyer – 02/01/2022

I always thought Machine Learning was super complicated, but after reading this book, I realized it’s not as intimidating as I expected. The explanations are super clear!