Top 10 Must-Read Books For Machine Learning
The best books for Machine Learning that one should not miss to become from a beginner to an expert.
Machine Learning has its deep roots along with Deep Learning, Data Mining, Artificial Intelligence, etc. Here we provide Top 10 books on Machine Learning!
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Machine Learning is a broad aspect and you can check the comparison of Machine Learning vs Deep Learning. Along with this to have an insight into Linear Regression with Python Click or Artificial Intelligence Click.
Get the Top 10 books on Machine Learning to start your learnings from scratch and gain skills and knowledge.
An Overview of Machine Learning
How great it is to make machines learn on their own without the requirement of being continuously programmed for all cases. This is where Machine Learning comes into play, with pacing technology it has become a crucial part of technical growth. We provide the machine with some sample data set on which it learns how to analyze and give output. We can say that the machine learns from the sample data sets provided to produce accurate results. It uses all the historical data for analysis and involves various statistical operations to produce the result. One of the common examples of machine learning is a chatbot. It is trained by providing a data set and making the program learn how to analyze the data and give proper output. Then as per the learning of the chatbot based on the type of data used for its training, it produces the output.
This requires training the machines with different types of data set so that they can learn prediction and given output in a much better way. In this process, the machines when provided with a practice data set may undergo various errors and these errors are corrected so that the machine learns how to give accurate output.
Why is Machine Learning Important?
Machine learning is rapidly growing and enhancing day-by-day as this is one of the most powerful domains for technical growth and progress. Machine learning has made machines capable of learning on their own and perform specific operations on the raw data provided with the best possible technique. It has contributed towards business development as machines are now capable of working on their own without by-and-by human interference to specify the particular algorithm to analyze a particular problem. Machine Learning has made various processes like analyzing big volumes of data, computational processing, etc. easy, effective, and cost-efficient.
In traditional computational techniques, mistakes were there, but with Machine Learning the probability of mistakes has reduced and we get quick, efficient, and accurate results. The output is automated and in a systematic manner. Machine learning is important as it has revolutionized the whole industry with drastic development and progress.
Being a subset of Artificial Intelligence(AI), Machine Learning is used to analyze and compute the data patterns to get proper results for better decision making. Instead of having a bulk amount of data for each test case and making new algorithms as test cases and conditions increase in the future, we have a trained machine that will learn on its own just like humans. This reduces hefty manual work of changing and updating all the algorithms time-to-time as test cases increase.
One of the important things is managing and processing a bulk amount of data. Though we have various traditional techniques to analyze such a huge amount of data as time increases, the industry demands pacing solutions with accuracy and effectiveness, in that situation all the previous techniques are no longer effective. At that time the importance of machine learning comes into play. Machine learning is very important nowadays, all the industrial and business work relies on the effective solution provided by machine learning.
Though there are many other benefits of machine learning when we link it with different fields like data science, artificial intelligence, deep learning, etc. It has made a huge contribution in all such fields by automating the machines and making the analysis and procedure more effective, efficient, feasible, and by prioritizing time management.
How to start Machine Learning?
Machine learning is a very broad domain with numerous technologies, procedures, mechanisms, and algorithms related to all the different fields to which machine learning is related like Data Science, Artificial Intelligence, Cyber Security, Deep learning, and much more. So it becomes quite confusing from where to start the journey of developing and learning.
The first basic step is to understand the prerequisite. It includes knowledge of mathematics. For starting with machine learning one should be good at mathematics, his logical reasoning and statistics should be strong. All the related concepts should be clear and he should be able to solve the relevant real-life problems. Once a person is ready with this, the next step is getting familiar with the specific programming language that the person wants to work with. He can choose R or Python, anything with which he is comfortable and should be good at it. Once this basic criterion learning is met, he can move ahead.
Then comes learning the basic concepts of machine learning which is a very important step. There are various terminologies used in machine learning as well as there are various types of machine learning, so one needs to choose these things according to his interest and start learning.
Learning the concepts of machine learning is a crucial step and requires the best knowledge source. Learning the concepts from books is one of the best ways as books are a vast source of knowledge with minute details about the topics and covering all possible topics. Here we have provided the top 10 books on Machine Learning ranging from the level of beginners to advanced specialization. These are the widely used and recommended books on machine learning providing in-depth knowledge and practice problems for you to boost-up and enhance your skills to become an expert in this field.
Top 10 Books on Machine Learning
1. The Elements of Statistical Learning – Data Mining, Inference, and Prediction
Author Name: Trevor Hastie, Robert Tibshirani, and Jerome Friedman
Latest Edition: 2nd, illustrated
Publisher: Springer Science & Business Media, 2009
This book is majorly for enthusiastic data scientists interested in data mining and the business industry. This book is a framework of statistics and concept building. It includes the techniques and problem statements to make your concepts strong while dealing with real-life problem scenarios. As we know that, we have vast amounts of data with us that need to be pre-processed and clustered in a proper set for further processing to get valuable insight into the data. For this, we require various mechanisms and proper data mining techniques. Also in this field, one requires sharp skills in statistics and other relevant mathematical concepts, so this book guides you from scratch to develop your mathematics skills and dive deep into the real-life scenario where you can start thinking to boost your concepts and implement your logic and idea for a particular problem statement.
It includes various topics covering linear Methods for Regression, Neural Networks, Supervised Learning, Linear Methods for Classification, Additive Models, Trees, and Related Methods, Boosting, and Additive Trees, Unsupervised Learning, High Dimensional Problems, and much more. With these topics, this book gives a wide approach in various fields like technology, business, financial department, biology, etc. It involves various algorithms and techniques to strengthen your concepts and make you master in statistics.
2. Machine Learning for Dummies
Author Name: John Paul Muller and Luca Massaron
Latest Edition: 2nd, illustrated
Publisher: John Wiley & Sons, 2016
This book is for beginners who want to master machine learning including the fields of email-spam filtering, automation, credit scoring, and much more. This book is a good start for beginners who want the skills of Big Data along with descriptive and predictive analytics. It includes the topics for understanding machine learning followed by the fields where you can apply machine learning and get its insight. Then you will continue with learning machine learning skills, solving business-related problems, and getting future predictions on Machine Learning.
The topics involved are Big Data Context with Machine Learning, Descriptive Analytics, Predictive Analytics, Role of Statistics and Data Mining with Machine Learning, Supervised and Unsupervised Learning, Reinforcement Learning, Neural Networks, and Deep Learning, Recognising Patterns, Role of Algorithms and much more with real-life problems based on cybercrime, health, etc.
In this way, it provides a broad overview of Machine Learning at the start so that you can develop your basic skills and move further to enhance your concepts and apply them for real-life problem scenarios.
3. The Hundred-Page Machine Learning Book
Author Name: Andriy Burkov
Latest Edition: 1st
Publisher: Andriy Burkov
This book is famous for providing a brief overview of Machine Learning in just a hundred pages. All the concepts are clearly described so that the reader might relate it with the real-life problem statements. This book is the best choice for the readers who want to either start the journey from scratch to get a basic overview of machine learning or want to brush up their concepts. This is a good choice to complete the Machine learning overview in just 100 pages!
It is a short book that covers everything that you should know about Machine Learning at the initial stage. It provides sufficient data at the beginner level and makes it easy to keep notes of what you might require to revisit to freshen your memory. It explains maths in a great way, ranging from regression, supervised and unsupervised learning to Machine Learning algorithms, basic practice, neural networks, and deep learning, and much more.
This is highly recommended for Data Science aspirants with topics like Support Vector Machines, Ensemble Methods, Gradient Descent, Cluster Analysis, Dimensionality Reduction. Autoencoders, Hyperparameter Tuning, and much more.
4. Machine Learning for Absolute Beginners: A Plain English Introduction
Author Name: Oliver Theobald
Latest Edition: 2nd
Publisher: Scatterplot Press
This book covers all the necessary topics in a simplified manner including problems based on Python. This is a great choice as it simplifies all the topics so that the reader can get a clear visualization of the concepts and ideas involved in it. It is good when you want to start your career in Machine Learning.
All the topics like regression, Neural Networks, Random Forests, Decision Trees, Machine Learning Algorithms, Big Data, Machine Learning Tools, Machine Learning Basics, Data Science, Big Data Analytics, Machine Learning Cloud, etc. are provided in simple and easy language. It includes a great way of approaching mathematical concepts and has used good mechanisms to build and enhance the problem-solving capability in learners. In this way it comes with a full package of learning, building from scratch, and sharpening the skills that one has gained.
It comes with Machine Learning applications in the Data Science field and Business opportunities, one who wants to develop in this domain should refer to this book for in-depth knowledge and essential skills.
5. Introduction to Machine Learning with Python: A Guide for Data Scientists
Author Name: Andreas C. Muller & Sarah Guido
Latest Edition: 1st
Publisher: O’Reilly Media
Machine Learning is a vast domain with its application in numerous fields and Python is one of the most popular programming languages, rapidly growing, effective, and efficient to use. Python has revolutionized the technology and programming domain with its high capability to perform various functions quickly with a lesser amount of time delay, hence Python is the most preferred programming language when it comes to Machine Learning. Python is the most widely used programming language in the domain of Machine Learning, Artificial Intelligence, Deep Learning, and what not!
This book is the best choice if you want to learn Python for the Machine Learning domain as this will take you from scratch to an advanced specialization level with conceptual clarity and great problem examples which will enhance your skills. This includes the topics – sci-kit-learn and its installation, Python 2 versus Python 3, Classifying Iris species, Supervised Learning, Unsupervised learning and Preprocessing, Representing Data, Model Evaluation and Improvement, Algorithm Chains and pipelines and much more o boost your skills and learning.
6. Python Machine Learning: Machine Learning and Deep Learning with Python, sci-kit-learn, and TensorFlow, 2nd Edition
Author Name: Sebastian Raschka and Vahid Mirjalili
Latest Edition: 2nd
Publisher: Packt Publishing limited
As Machine Learning is growing at a very high pace it is necessary to learn all the associated concepts like Deep Learning, Big Data Analytics, Neural networks, etc. if you want to pursue this field. The competition is very high and only clear concepts and understanding are helpful. For the same, this book provides wide and simplified learning with the most popular programming language, Python. As competition is growing, the need for an efficient and compatible programming language is a necessity and the same Python is the best date option.
This book will turn a novice into a specialized in Machine Learning and Deep Learning with Python, sci-kit-learn, and TensorFlow. It includes various important topics like Data Analysis, Building Intelligent Machines, Making Predictions about Future, Regression, reinforcement Learning, Supervised and Unsupervised Learning, Dimensionality Reduction for Data Compression, and much more.
7. An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Author Name: Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
Latest Edition: 1st
Publisher: Springer Science & Business Media, 2013
To get a broad overview of Statistics in Machine Learning ranging from finance for Business to biology for the health sector. It provides a systematized way of learning and concept development. For Business Analysts, mathematical concepts, especially statistics, are quite important. One should have deep insight into this domain, clear concepts, and wide knowledge. These books provide the same requirements along with various practice problems. For statistics, R is the best programming language to get started with.
The topics included are Shrinkage approaches, Tree-based Methods, Regression Classification, Resampling Methods, Clustering, and much more to make you an expert in Data Preprocessing, Data Mining, and Big Data Analytics. To enhance your skills and boost your knowledge this is one of the best options if you want to kickstart your Business or Data Science career with R.
8. Machine Learning Yearning
Author Name: Andrew Ng
Latest Edition: 1st
If you are one who prefers eBooks then this is the best option for you! In this book, the processes related to Machine Learning are given priority over implementations. This comes with a wide variety of problems that you can solve to check your knowledge and skills. This is one of the widely preferred books as it focuses more on skills by practice problems so that learners will get more experience to clear their concepts by actually performing the steps mentioned.
It includes topics like Characteristics of Optimization Metrics, error Analysis, Bias Variance Trade-off, Optimization Verification Testing, End-to-End Learning versus Pipeline Learning. Pipeline Learning and much more. It also provides a comparison of machine-level performance to human-level performance thus providing a wide view of the requirements and fields of machine learning.
9. Machine Learning for Hackers: Case Studies and Algorithms to Get You Started (1st Edition)
Author Name: Drew Conway and John Myles White
Latest Edition: 1st
Publisher: O’Reilly Media
If you are an experienced programmer from any field then this book is for you! This book will help you to learn and understand the core concept of Machine Learning and how it works, how machines are trained, and how they learn from the data to get automated. This book gives a brief detail and step by step explanation of all these concepts for better understanding and visualization. It uses the R programming language for analyzing a data set and writing machine learning algorithms. In this way, using the R language, it trains the machine to learn from the sample dataset and become able to work like humans for future datasets provided.
It includes topics – data classification, optimization, prediction, data preprocessing, and much more. The concepts are clear and simple so that learners will be able to achieve their goals without any problem.
10. Pattern Recognition and Machine Learning (1st Edition) – Christopher M. Bishop
Author Name: Christopher M. Bishop
Latest Edition: 1st
This is one of the best books that you will find for Pattern Recognition and Machine Learning. It provides fast and accurate solutions to the most difficult problems where most of the algorithms fail. It uses inference algorithms and graph models to describe probability distributions. This is the best book even if you are a novice, it will lead you to greater heights with strong concepts and clear visualization of thoughts and ideas.
The topics included are Polynomial Curve Fitting, Probability Theory, Probability Distributions, Linear Model for Regression, Linear Model for Classification, Neural Networks, Kernel Methods, Sparse Kernel Machines, Graphical Models, Mixture Model, and EM, Approximate Inference, Sampling Methods, Continuous Latent Variables, Sequential Data and much more. This book is the best fit for pattern recognition and Machine Learning.
So this includes the basic steps to learn Machine Learning. Once you are ready with your mathematical concepts, especially of statistics and programming skills, then you can refer to any one of the above-mentioned books as per your requirement. They will lead you to step by step towards development and will help you to enhance your skills to become a master in Machine Learning. All you require is continuous enthusiasm, the urge to learn and practice all the concepts thoroughly.
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