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

Charlotte Davis Verified Buyer – 09/02/2023

As someone who struggles with technical topics, I found this book incredibly helpful and easy to digest!
Rated 5 out of 5

Lily Clark Verified Buyer – 18/01/2023

The book is structured so well—you can easily follow along without feeling lost. Great for self-study!
Rated 5 out of 5

Evan Turner Verified Buyer – 14/01/2023

I love how the book includes examples from different industries. It really helps you see ML’s real-world impact!
Rated 5 out of 5

Emma Williams Verified Buyer – 13/01/2023

It’s packed with useful information but never feels too dense. A great balance of depth and simplicity!
Rated 5 out of 5

Nathan White Verified Buyer – 09/01/2023

This book gave me the confidence to start exploring ML on my own. A great first step into the field!
Rated 5 out of 5

Penelope Jackson Verified Buyer – 24/12/2022

After reading this, I finally understand the difference between supervised and unsupervised learning. Super helpful!
Rated 5 out of 5

Oliver Johnson Verified Buyer – 24/12/2022

The book gives just the right amount of detail—not too overwhelming, but still informative enough to be valuable.
Rated 5 out of 5

Clara Taylor Verified Buyer – 20/12/2022

ML always felt intimidating, but this book made it approachable and easy to grasp. Highly recommend!
Rated 4 out of 5

Aaron Johnson Verified Buyer – 18/12/2022

I had no background in programming, but this book still made sense to me. That’s how well it’s written!
Rated 5 out of 5

Grace Mitchell Verified Buyer – 18/12/2022

I now understand why ML is such a game-changer. This book made me excited about its possibilities!