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

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‘…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

William Lee Verified Buyer – 21/04/2022

I never thought I’d be interested in Machine Learning, but this book completely changed my mind. It’s easy to understand and super practical—definitely worth reading!
Rated 5 out of 5

Zoey Carter Verified Buyer – 16/04/2022

After reading this, I realized ML isn’t just about technology—it has so many real-world applications. This book really broadened my perspective!
Rated 5 out of 5

Gabriel Nelson Verified Buyer – 13/04/2022

This is the first time I’ve read about ML without feeling overwhelmed. The book is truly made for beginners.
Rated 5 out of 5

Ella Walker Verified Buyer – 11/04/2022

The author writes as if they’re having a conversation with you. It doesn’t feel like a textbook at all—it’s super engaging!
Rated 5 out of 5

Samuel King Verified Buyer – 08/04/2022

Concepts like Gradient Descent and Overfitting used to confuse me, but this book explains them so well. Everything makes sense now!
Rated 5 out of 5

Amelia Adams Verified Buyer – 30/03/2022

I appreciate how the book connects theory with real-world applications. It’s not just about learning—it’s about actually using ML.
Rated 5 out of 5

Daniel Nguyen Verified Buyer – 16/03/2022

I wish I had found this book earlier! It would have saved me so much time trying to understand ML. It’s concise, yet explains everything clearly.
Rated 5 out of 5

Nora Clark Verified Buyer – 03/03/2022

What I love most is that the book doesn’t dump too much information at once. It introduces concepts gradually, making them easy to digest.
Rated 5 out of 5

James Scott Verified Buyer – 26/02/2022

This book made me genuinely excited to study ML. I finished it in two days because I couldn’t put it down!
Rated 5 out of 5

Lily Baker Verified Buyer – 22/02/2022

The section on evaluating models is fantastic. It helped me understand how to measure the accuracy of an algorithm without feeling overwhelmed.