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

Nathan Harris Verified Buyer – 06/08/2022

The writing is friendly, the explanations are clear, and the content is super practical. What more could you ask for?
Rated 5 out of 5

Ruby Wood Verified Buyer – 04/08/2022

The future is AI-driven, and this book made me feel like I can actually be part of it. Such a great read!
Rated 5 out of 5

Logan Murphy Verified Buyer – 02/08/2022

I’ve tried online courses, YouTube tutorials, and blogs, but this book is the best introduction to ML I’ve found.
Rated 5 out of 5

Sophia Bennett Verified Buyer – 21/07/2022

The way this book explains concepts like model training and bias in data is so easy to follow. I actually enjoyed learning about it!
Rated 5 out of 5

Harrison Simmons Verified Buyer – 20/07/2022

I had some experience with Python, but ML was still a mystery to me. This book made it all come together!
Rated 5 out of 5

Avery Richards Verified Buyer – 19/07/2022

I highly recommend this to anyone who wants to start learning about AI and ML without feeling overwhelmed.
Rated 5 out of 5

Samuel Foster Verified Buyer – 13/07/2022

Machine Learning always seemed like a black box to me, but this book broke it down in a way that finally makes sense.
Rated 5 out of 5

Leah Cooper Verified Buyer – 11/07/2022

I thought this book would just be another boring intro, but it’s actually engaging and well-structured. I finished it in a few days!
Rated 5 out of 5

Jack Sanchez Verified Buyer – 10/07/2022

The sections on Supervised vs. Unsupervised Learning were especially helpful. I finally understand the difference!
Rated 5 out of 5

Emily Perez Verified Buyer – 08/07/2022

I feel so much more confident talking about Machine Learning now. This book helped me understand it without getting lost in technical jargon.