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Online Learning: Top 5 eBooks for Machine Learning Experts
Technology gives us all the instruments to learn from home, easily and comfortably. Here are the top 5 eBooks for experts who want to improve their skills at machine learning
In these days of fear and mourning, COVID-19 has forced us to rethink our way of life and usual daily routines. But as every cloud has a silver lining, self-isolation has also taught us that technology gives us all the instruments we need for proper online learning from home.
Although smart working and remote working are not new concepts for people in the IT world, staying at home means that we now have a unique opportunity to leverage the full power of technology to improve our lives.
Learning is part of our process of growth, and instead of wasting time on the internet watching cat pictures*, this forced lockdown time is a great opportunity to grow and improve by learning something new (*actually, watching cat pictures is another great way to spend your spare time.)
Here are the top 5 eBooks for experts who want to improve their skills at machine learning (ML).
Note: Not quite an expert? We've also got "Machine Learning from Home: Top 5 eBooks for Beginners."
Written by: Giuseppe Bonaccorso
Robust and well-developed ML algorithms are the core of any artificial intelligence (AI).
They are the foundation of any smart machine intelligence as they teach it how to collect data, how to improve its strategies, and how to increase its efficiency over time.
Reading this book by Italian data scientist Giuseppe Bonaccorso will teach you how to understand how an ML algorithm works, how to implement them from and supervised, semi-supervised, unsupervised, and RL domains, as well as how to solve end-to-end ML problems with practical use case scenarios.
You will work with autoencoders (AE) and all the most modern Python libraries as you train and optimize neural networks and evaluate model accuracy.
You will learn how to extract meaningful information from all kind of data by leveraging Hidden Markov models, Bayesian models, and many other different algorithms.
Written by: Steven F. Lott
Object-oriented programming (OOP) isn’t an easy science to master, but its complexities make this discipline particularly suited to the needs of ML programming.
This book will teach you how you can apply and implement the general principles as well as the most advanced concepts of OOP in Python and Python 3.x.
Learning how to solve common problems and create high-performance programs in the Python environment through practical examples is just the beginning.
You will learn how to create your own custom classes, develop new number types beyond the built-in classes of numbers, maintain and reuse code, and build RESTful web services with FLASK.
On top of that you will master the complexities of the __init__() method, the Python 3's abstract base, and the SOLID principles.
Written by: Ivan Vasilev
If you read the previous book and/or mastered the Python ecosystem already, it only makes sense to start learning how to apply your knowledge of this programming language to the most advanced, state-of-the art deep learning domains and artificial neural network (ANN) architectures.
This eBook will guide you through the theory and math behind ANNs, as well as how to train convolutional neural network (CNN) models.
You will learn how implement generative adversarial networks (GANs), tackle complex natural language processing (NLP) challenges, and master advanced DL techniques such as meta-learning.
By the end of this amazing book you will be able to leverage ANNs for complex, real-world applications such as structured data processing or autonomous vehicles.
Written by: Vipul Tankariya, Bhavin Parmar
If you want to pass the Amazon Web Services (AWS) Certified Developer – Associate exam, this is the right book for you.
Written by two of the top-tier, certified AWS subject-matter experts Vipul Tankariya and Bhavin Parmar, this eBook will guide you through all the necessary steps to become a true master of AWS IAM services, DynamoDB, storage services such as Glacier and CloudFront, and much more.
You will learn by exploring real-world scenarios as you become familiar with microservices, the Virtual Private Cloud (VPC), up to Elastic Beanstalk and AWS lambda.
All packed with useful tips and tricks to improve your chances to pass the test, and many mock tests to check if you’re prepared enough before the final exam.
Written by: Benjamin Johnston, Aaron Jones, Christopher Kruger
When structured data is unavailable, unsupervised learning is a useful and efficient solution to your problems.
Knowing how to use it in a Python environment is what this eBook is going to teach you through practical examples and best practices.
From understanding how to use basic clustering to aggregate information in datasets to applying dimensionality reduction and other ANN techniques, you are going to learn how to enhance and optimize your unsupervised learning model.
Johnston, Kruger and Jones will provide you with a lot of real-world use cases such as how to perform a Market Basket Analysis, identify relationships between different products, or mine trending topics on social networks.
In this moment of crisis, our computers and smartphones have quickly become our new best friends and our windows to the outside world. They also give us the unique opportunity to learn, grow and acquire new tech skills.
Don’t waste any more self-isolation time, and grab one of these eBooks to skyrocket your career in ML when these hard times are over!
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- Why is Python so popular in machine learning?
- What is the difference between deep learning and machine learning?
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- How does machine learning support better supply chain management?