Machine learning (ML) is a key branch of artificial intelligence (AI) that focuses on building intelligent applications. Data scientists learn from the varying complexity of data and improve the accuracy and performance over time without being programmed to do so. As per an Indeed report, the growth rate for machine learning jobs is about 344%, while automation remains the trending face of technology. With the increasing maturity and automation of ML algorithms and tools, AutoML and MLOps will continue to gain even more prominence, where we could expect ML Engineers to be in the highest demand in 2021. This post will look at the top 10 exciting books relating to new research and application areas in ML.
SwissCognitive Guest Blogger: Sunith Shetty, Group Product Manager, Packt
Machine learning is growing exponentially and fueling new and improved algorithms, helping researchers and developers to make an impact in new and emerging topic areas. Our mission here is to help the global developer community stay up-to-date and put technology to work in new ways. Here are 10 exciting machine learning books to read in 2021:
Interpretable models allow developers to comprehend why certain decisions or predictions have been made. This book helps you understand the key aspects and challenges of machine learning interpretability, how to overcome them with interpretation methods, and how to leverage them to build fairer, safer, and reliable models.
Key features:
- Learn how to extract easy-to-understand insights from any machine learning model
- Become well-versed with interpretability techniques to build fairer, safer, and more reliable models
- Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models
BERT has revolutionized the world of natural language processing (NLP) with promising results. This book is an introductory guide that will help you get to grips with Google BERT’s architecture, along with practical examples.
Key Features
- Explore the encoder and decoder of the transformer model
- Become well-versed with BERT along with ALBERT, RoBERTa, and DistilBERT
- Discover how to pre-train and fine-tune BERT models for several NLP tasks
Deep learning is driving the AI revolution, and PyTorch is making it easier than ever for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models.
Key Features
- Understand how to use PyTorch 1.x to build advanced neural network models
- Learn to perform a wide range of tasks by implementing deep learning algorithms and techniques
- Gain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much more
MLOps is a systematic approach to building, deploying, and monitoring machine learning (ML) solutions. This book presents comprehensive insights into MLOps with real-world examples to help you write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production.
Key Features
- Become well-versed with MLOps techniques to monitor the quality of machine learning models in the production
- Explore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed models
- Perform CI/CD to automate new implementations in ML pipelines
Automated Machine Learning with Microsoft Azure helps you to build high-performing, accurate machine learning models in record time. It allows anyone to easily harness the power of artificial intelligence and increase the productivity and profitability of their business. With a series of clicks on a guided user interface (GUI), both novices and seasoned data scientists can train and deploy machine learning solutions to production with ease.
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Key Features
- Create, deploy, productionalize, and scale automated machine learning solutions on Microsoft Azure
- Improve the accuracy of your ML models through automatic data featurization and model training
- Increase productivity in your organization by using artificial intelligence to solve common problems
Prophet enables Python and R developers to build scalable time-series forecasts. This book will help you to implement Prophet’s cutting-edge forecasting techniques to build advanced and accurate forecast models with very few lines of code.
Key Features
- Learn how to use the open-source forecasting tool Facebook Prophet to improve your forecasts
- Build a forecast and run diagnostics to understand forecast quality
- Fine-tune models to achieve high performance, and report that performance with concrete statistics
TPOT is a Python automated machine learning tool used for optimizing machine learning pipelines using genetic programming. Automating machine learning with TPOT enables individuals and companies to develop production-ready machine learning models cheaper and faster than with traditional methods.
Key Features
- Understand parallelism and how to achieve it in Python.
- Learn how to use neurons, layers, and activation functions and structure an artificial neural network
- Tune TPOT models to ensure optimum performance on previously unseen data
AutoKeras is an AutoML open-source software library that provides easy access to deep learning models. If you are looking to build deep learning model architectures and perform parameter tuning automatically using AutoKeras, then this book is for you. You will be able to confidently use AutoKeras to design your own custom machine learning models in your company.
Key Features
- Design and implement your own custom machine learning models using the features of AutoKeras
- Learn how to use AutoKeras for techniques such as classification, regression, and sentiment analysis
- Get familiar with advanced concepts as multi-modal, multi-task, and search space customization
Artificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. The Artificial Intelligence with Python Cookbook will teach you how to solve complex problems with the help of independent and insightful recipes, ranging from the essentials to new and advanced methods.
Key Features
- Get up and running with artificial intelligence in no time using hands-on problem-solving recipes
- Explore popular Python libraries and tools to build AI solutions for text, sounds, and images
- Implement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much more
Generative Pre-trained Transformer 3 (GPT-3) is a highly advanced language model from OpenAI that can generate written text, which is virtually indistinguishable from text written by humans. Whether you have a technical or non-technical background, this book will help you understand and start working with GPT-3 and the OpenAI API.
Key Features
- Understand the power of potential GPT-3 language models and the risks involved
- Explore core GPT-3 use cases such as text generation, classification, and semantic search using engaging examples
- Plan and prepare a GPT-3 application for the OpenAI review process
Final Notes
Learning new methodologies and topics in machine learning is important because of its incredible ability to provide solutions to complex problems efficiently and quickly in different leading domains such as healthcare, finance, automation, chatbot, and IoT. Getting exposure to modern machine learning areas will help you learn existing and new machine learning techniques easily and also differentiate yourself from others in the competitive market.
About the Author:
Sunith Shetty is a data science fanatic. He helps IT developers to become thought leaders in developer communities within 12 months by writing an authoritative book. He is currently working with machine learning and AI developer communities to design and create book value propositions on popular and emerging technologies.
Machine learning (ML) is a key branch of artificial intelligence (AI) that focuses on building intelligent applications. Data scientists learn from the varying complexity of data and improve the accuracy and performance over time without being programmed to do so. As per an Indeed report, the growth rate for machine learning jobs is about 344%, while automation remains the trending face of technology. With the increasing maturity and automation of ML algorithms and tools, AutoML and MLOps will continue to gain even more prominence, where we could expect ML Engineers to be in the highest demand in 2021. This post will look at the top 10 exciting books relating to new research and application areas in ML.
SwissCognitive Guest Blogger: Sunith Shetty, Group Product Manager, Packt
Interpretable Machine Learning with Python
Interpretable models allow developers to comprehend why certain decisions or predictions have been made. This book helps you understand the key aspects and challenges of machine learning interpretability, how to overcome them with interpretation methods, and how to leverage them to build fairer, safer, and reliable models.
Key features:
Getting Started with Google BERT
BERT has revolutionized the world of natural language processing (NLP) with promising results. This book is an introductory guide that will help you get to grips with Google BERT’s architecture, along with practical examples.
Key Features
Mastering PyTorch
Deep learning is driving the AI revolution, and PyTorch is making it easier than ever for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models.
Key Features
Engineering MLOps
MLOps is a systematic approach to building, deploying, and monitoring machine learning (ML) solutions. This book presents comprehensive insights into MLOps with real-world examples to help you write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production.
Key Features
Automated Machine Learning with Microsoft Azure
Automated Machine Learning with Microsoft Azure helps you to build high-performing, accurate machine learning models in record time. It allows anyone to easily harness the power of artificial intelligence and increase the productivity and profitability of their business. With a series of clicks on a guided user interface (GUI), both novices and seasoned data scientists can train and deploy machine learning solutions to production with ease.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Key Features
Forecasting Time Series Data with Facebook Prophet
Prophet enables Python and R developers to build scalable time-series forecasts. This book will help you to implement Prophet’s cutting-edge forecasting techniques to build advanced and accurate forecast models with very few lines of code.
Key Features
Machine Learning Automation with TPOT
TPOT is a Python automated machine learning tool used for optimizing machine learning pipelines using genetic programming. Automating machine learning with TPOT enables individuals and companies to develop production-ready machine learning models cheaper and faster than with traditional methods.
Key Features
Automated Machine Learning with AutoKeras
AutoKeras is an AutoML open-source software library that provides easy access to deep learning models. If you are looking to build deep learning model architectures and perform parameter tuning automatically using AutoKeras, then this book is for you. You will be able to confidently use AutoKeras to design your own custom machine learning models in your company.
Key Features
Artificial Intelligence with Python Cookbook
Artificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. The Artificial Intelligence with Python Cookbook will teach you how to solve complex problems with the help of independent and insightful recipes, ranging from the essentials to new and advanced methods.
Key Features
Exploring GPT-3
Generative Pre-trained Transformer 3 (GPT-3) is a highly advanced language model from OpenAI that can generate written text, which is virtually indistinguishable from text written by humans. Whether you have a technical or non-technical background, this book will help you understand and start working with GPT-3 and the OpenAI API.
Key Features
Final Notes
Learning new methodologies and topics in machine learning is important because of its incredible ability to provide solutions to complex problems efficiently and quickly in different leading domains such as healthcare, finance, automation, chatbot, and IoT. Getting exposure to modern machine learning areas will help you learn existing and new machine learning techniques easily and also differentiate yourself from others in the competitive market.
About the Author:
Sunith Shetty is a data science fanatic. He helps IT developers to become thought leaders in developer communities within 12 months by writing an authoritative book. He is currently working with machine learning and AI developer communities to design and create book value propositions on popular and emerging technologies.
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