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Why is Machine Learning Important Today?

Machine learning algorithms are learnings from the data that is provided by us.  For example, every time new data is provided, it improves the model’s accuracy and efficiency to make better decisions. It also helps in subsequent training improvement.  To illustrate, let us take a ride down the list of top 20 Machine Learning Books, for getting a better understanding of today’s mechanism.  Moreover, data is collected and curated by renowned authors in a language easily understandable by all.

1. The Hundred-Page Machine Learning Book

Book Overview – 

To begin with, this book is written by Andriy Burkov. Furthermore, it gives insights into how we can understand Machine Learning topics in a mere 100 pages document.  Furthermore, being one of the 20 best machine learning books, it is in an easy-to-understand manner, this book is endorsed by reputed thought leaders.  Nonetheless, it is one of the best books on machine learning.  To find out what’s more, click Amazon to get this book.

Topics Covered:

• Anatomy of a learning algorithm
• Fundamental algorithms
• Neural networks and deep learning
• Other forms of learning
• Supervised learning and unsupervised learning

Benefits of this book – 

To illustrate, post completion, you will be able to build and appreciate complex AI systems and clear ML-Based interviews.  Nonetheless, you would also be able to start your own ML Business.  Indeed, you heard that right.

2. Programming Collective Intelligence With Building Smart Web 2.0 Applications

Book Overview – 

Written by Toby Segaran, in short, it is one of the best books to begin your understanding of machine learning. Furthermore, this book gives you more insight into the use of Python as the vehicle for delivering knowledge to its readers. Nonetheless, this book being in the top 20 machine learning books, has gathered information about creating ml efficient algorithms, creating programs for accessing data from websites and inferring the gathered data.  To find out what’s more, click Amazon to get this book.

Topics Covered:

• Bayesian Filtering
• Collaborative filtering techniques
• Evolving intelligence for problem-solving
• Methods for detecting groups or patterns 
• Non-negative matrix factorization
• Search engine algorithms
• Support vector machines
• Ways to make predictions

Benefits of this book- 

This book takes you into the world of machine learning and statistics, also explaining to you how to conclude user experience, marketing, personal tastes, and human behaviour in general.  Furthermore, each algorithm gives clear and concise codes that can be used on your website.  Nonetheless, it has information for search engine features like crawlers, indexers, query engines, and PageRank algorithms.

3. Machine Learning for Hackers with Case Studies and Algorithms

Book Overview – 

In short, this book of the 20 machine learning books, is meant for experienced programmers who are interested in crunching data.  Conversely, Hackers refer to adroit mathematicians.  On the contrary, this book contains data analysis in R.  Furthermore, it has details on using advanced R in data wrangling.  To find out what’s more, click Amazon to get this book.

Topics Covered:

• Developing naïve Bayesian Classifier
• Linear Regression and also, optimization techniques
• Using R for data querying

Benefits of this book – 

The most important highlight of this machine learning book is the inclusion of apposite case studies which highlight the importance of using machine learning algorithms. Lastly, the book provides real-life examples of how to make ml easier and faster.

Book Overview- 

Written by Tom M Mitchell, however, this book offers a comprehensive overview of machine learning theorems with pseudocode summaries of respective algorithms.  To find out what’s more, click Amazon to get this book.

Topics Covered:

• Genetic Algorithms, also, as inductive logic programming
• Introduction to primary approaches of machine learning
• Machine learning concepts and techniques, in addition to, re-enforcement learning

Benefits of this book – 

To illustrate, it provides lots of examples along with case studies to make readers’ understanding of ml algorithms, simple.  To summarize, if you wish to take your career forward in machine learning, then, this book is a must-have.

5. Elements of Statistical Learning With Data Mining, Inference, and Prediction

Book Overview – 

Specifically, readers with a keen interest in statistics should opt for this book.  Moreover, this book emphasizes mathematical deviations for defining the underlying logic of an ml algorithm.  To find out what’s more, click Amazon to get this book.

Topics Covered:

• Ensemble Learning
• High-dimensional problems
• Linear methods for classification and regression
• Model inference and averaging, as well as, neural networks
• Random forests, furthermore, supervised and unsupervised learning

Benefits of this book – 

Though the book is not beginner-friendly and complex to digest, yet, you can purchase the first edition of the same book, which gives the same concepts but in a beginner-friendly way.  To summarize, it is An Introduction to Statistical Learning.

6. Learning from Data: A Short Course

Book Overview –

In particular, if you want to get a good understanding of engineering mathematics, this is the right book for that.   This book prepares its readers to better understand the complexity of machine learning concepts.  To find out what’s more, click Amazon to get this book.

Topics Covered:

• Error and Noise
• Kernel methods
• Overfitting
• Radial basis functions
• Regularization
• Support vector machines
• Validation

Benefits of this book – 

This book comes straight to the point, providing explanations succinct, to explaining.  Furthermore, you can also watch online tutorials of Mr Yaser Abu Mostafa.

7. Pattern Recognition & Machine Learning

Book Overview – 

This book serves as an excellent reference for understanding and using statistical techniques as well as pattern recognition.  Moreover, you need to have a sound understanding of linear algebra and multivariate calculus for understanding this book better. In short, click Amazon to buy this book.

Topics Covered:

• Approximate inference algorithms
• Bayesian method
• Intro to basic probability theory and pattern recognition and machine learning
• New models based on kernels

Benefits of this book – 

This book uniquely leverages graphical models by describing probable distributions.  But it doesn’t help to fasten the learning process.

8. Natural Language Processing with Python

Book Overview –

Firstly, this book is the backbone of machine learning systems.  Indeed, this is a guide using Python Programming Language to navigate you into NLTK.  NLTK is the popular suite of python libraries and programs for symbolic and statistical natural language processing for English and NLT in general.  To find out what’s more, click Amazon to buy this book.

Topics Covered:

• How human language works
• Integrated techniques from artificial intelligence and linguistics
• Linguistic data structures
• Natural language toolkits
• Parsing and semantic analysis
• Popular linguistic databases

Benefits of this book – 

Furthermore, it presents powerful python codes, demonstrating NLP, in a clear and precise manner.  Nonetheless, it makes it easy for readers to access well-annotated datasets for analyzing and dealing with unstructured data, linguistic structure in text and other NLP aspects.

9. Bayesian Reasoning and Machine Learning

Book Overview – 

Written by Mr David Barber, this book contains solutions for computer scientists interested in learning ml but do not have a background in calculus or linear algebra.  To find out what’s more, click Amazon to buy this book.

Topics Covered:

• Approximate interference
• Dynamic models
• Framework of graphical models
• Learning in probabilistic models
• Naïve Bayes algorithms
• Probabilistic reasonings

Benefits of this book –

This book has a vast variety of examples and exercises in Bayesian reasoning.  Moreover, the book is appropriate for both graduate and undergraduate computer science students.  To enhance it further, there is also a provision of online resources and a software package which includes demos and teaching material for instructors.

10. Understanding Machine Learning

Book Overview –

This book offers a structured introduction to machine learning. Furthermore, the book dives into the fundamental theories and algorithm paradigms of machine learning.  It also provides mathematical derivations for the same.  To find out what’s more, click Amazon to buy this book.

Topics Covered:

• The computational complexity of learning
• Convexity and stability
• Neural networks
• ML algorithms
• PAC-Bayes approach
• Stochastic gradient descent
• Structured output learning

Benefits of this book –

This book, as mentioned, can be easily understood by computer science students to non-expert readers in computer science, engineering, mathematics, and statistics.

11. Machine Learning for Absolute Beginners- A Plain English Introduction

Book Overview –

Written by Mr Oliver Theobald, this book is basically for people with no prior experience or exposure.  It provides a clear explanation and examples related to ml algorithms. To find out what’s more, click Amazon to buy this book.

Topics Covered:

• Basics of neural networks
• Clustering
• Cross-validation
• Data scrubbing techniques
• Ensemble modelling
• Feature engineering
• Regression analysis

Benefits of this book –

It is beneficial to students with no prior experience as it requires no background in coding or mathematics.  That’s the reason, everything mentioned in this book is given with examples and visuals.

12. Machine Learning for Dummies

Book Overview –

This book aims to make readers familiar with basic concepts and theories in an easy way.  This uses Python and R coding to demonstrate how to train machines to find and analyze results.  To find out what’s more, click Amazon to buy this book.

Topics Covered:

• Data preparation
• Machine learning techniques
• Supervised and unsupervised learning
• The machine learning cycle
• Training machine learning systems
• Tying machine learning methods to outcomes

Benefits of this book –

This book focuses on the practical, real-world applications of machine learning.  It also explains how ml facilitates email filters, fraud detection web searches and more.

13. Fundamentals of ML for Predictive Data Analytics

Book Overview –

This machine learning book contains the fundamentals of machine learning for predictive data analytics.  It helps in analyzing past and current events to make future predictions. To find out what’s more, click Amazon to buy this book.

Topics Covered:

• Error-based learning
• Information-based learning
• Probability-based learning
• Similarity-based learning
• Techniques for evaluating prediction models

Benefits of this book –

To summarize, this book gives algorithms, word examples and case studies.  Nonetheless, it makes use of an array of statistical techniques which help in analyzing past and current events for making future predictions.  Finally, the best part of the book is that each machine learning concept is explained with suitable examples.

14. Machine Learning in Action

Book Overview –

To illustrate, this book acts as a walkthrough for developers for writing their programs meant for acquiring data with aim of analysis.  Furthermore, it gives an in-depth discussion of the algorithms forming the basis of various machine learning techniques.  To find out what’s more, click Amazon to buy this book.

Topics Covered:

• Basics of machine learning as well as BigData, MapReduce
• FP growth as well as K-means clustering
• Logistic regression as well as support vector machines
• Tree-based regression

Benefits of this book –

Nonetheless, this book is preferred by a variety of people from undergraduates to professionals.  In addition, the concepts underlying the details of machine learning are thoroughly explained.  To summarize, it is a walkthrough for developers for writing their programs with the aim of analysis.

15. Data Mining- Practical Machine Learning Tools & Techniques

Book Overview –

To begin with, this book helps us discover patterns in large data in the fields of database systems, machine learning and statistics.  To find out what’s more, click Amazon to buy this book.

Topics Covered:

• Clustering as well as comparing data mining methods
• Instance-based learning as well as knowledge representation and clusters
• Linear methods as well as predicting performance
• Statistical modelling
• Traditional as well as modern data mining techniques

Benefits of this book –

Of the 20 machine learning books, furthermore, this covers more of the technical aspects of machine learning.  Nonetheless, it gives an in-depth insight into methods for obtaining data, using different inputs and outputs for evaluating results.  So, if you are planning to learn data mining, this is the best book you can purchase.

16. Machine Learning with TensorFlow

Book Overview –

Written by Nishant Shukla, TensorFlow however is a symbolic Math library, listed in 20 best machine learning books.  Furthermore, it is one of the top data science python libraries used for machine learning applications.  Nonetheless, it explains the ml basics with traditional classification, clustering, and prediction algorithms.  To find out what’s more, click Amazon to buy this book.

Topics Covered:

• Autoencoders as well as hidden markov models
• Convolutional, recurrent, as well as reinforcement neural networks
• Deep learning as well as linear regression

Benefits of this book –

To summarize, this book dives deeper into deep learning concepts making the readers ready for any kind of machine learning task.  Nevertheless, this is done by using the free and open-source TensorFlow library.

17. Machine Learning- Hands-on with Scikit-Learn, Keras and TensorFlow

Book Overview –

To illustrate, written by Aurelien Geron, this is the second edition of the hands-on machine learning books.  Alongside,it also gives an intuitive understanding of the various concepts and tools that are needed to develop smart, intelligent systems.  To find out what’s more, click Amazon to buy this book.

Topics Covered:

• Deep neural networks
• Deep reinforcement learning
• Linear regression
• Training models, including decision trees, ensemble methods
• Training neural nets

Benefits of this book –

Also, a Machine learning book containing concepts, tools, and techniques to build Intelligent systems, furthermore, all you need is programming experience to get going.  Each chapter contains numerous exercises for practical training of what you have learnt earlier.

18. Introduction to Machine Learning with Python- Guide for Data Scientists

Book Overview-

What’s more, if you are proficient in using Python and are interested in machine learning, this is the right book of the 20 machine learning books you can opt for.  As a result, it is a Kickstarter for your machine learning journey. Nevertheless this book has it all.  It also teaches you various practical ways of building your machine learning solutions.  To find out what’s more, click Amazon to buy this book.

Topics Covered:

• Advanced methods for model evaluation and parameter tuning
• Applications as well as fundamental concepts of machine learning
• Machine learning algorithms as well as methods for working with text data
• Pipelines for chaining models as well as encapsulating workflow
• Finally, representation of processed data

Benefits of this book –

Of these 20 machine learning books, nevertheless, it will give all the important steps for creating robust machine learning applications using Python and Scikit-learn library.  In short, you will require to have a good understanding of matplotlib and NumPy libraries.

19. Machine Learning- A Probabilistic Perspective

Book Overview –

Of the 20 best machine learning books, nonetheless, it is a fun learning machine book which flaunts nostalgic colour images and practical, real-world examples belonging to various domains like biology, computer vision, robotics, and text processing.  To find out what’s more, click Amazon to buy this book.

Topics Covered:

• Conditional random fields
• Deep learning
• L1 regularization
• Optimization
• Probability

Benefits of this book –

Unlike other machine learning books, this is like a cookbook which explains several methods by using graphical models for specifying ml models concisely and intuitively.

20. Python Machine Learning- A Technical Approach

Book Overview –

This is a beginner-friendly book out of the 20 best machine learning books, which gives details of the basics of machine learning as well as shares its importance of it in the digital sphere.  Nonetheless, it provides a wide variety of applications available.  To find out what’s more, click Amazon to buy this book.

Topics Covered:

• Basics of artificial intelligence
• Decision trees
• Deep neural networks
• Fundamentals of the python programming language
• Logistic regression

Benefits of this book –

To illustrate, it gives details on how to get started with the free and open-source programming language.  Furthermore, after completion, you will be able to code in Python and establish a wide variety of machine learning tasks.

Henry Harvin for Machine Learning using R (CMLP Course)

To begin with, Henry Harvin is an online educational platform, which provides a vast variety of online courses.  Thus, machine learning is one such course provided online.  

Topics Covered:

• Introduction to concept learning in addition to handling data in R
• Basic Statistics
• Data Manipulation in R, as well as, Graphical Representation of Data
• Decision Tree as well as Cluster Analysis

Benefits of the Course –

• To illustrate, there will be hands-on practical experience
• 5+ projects covered, subsequently followed by 32+ hours of online interactive session
• In addition, you will get Certificate on completion followed by internship
• Nonetheless, you will have unlimited access to abundant tools and techniques, video content and assessments.
• Finally, 100% placement guarantee

Henry Harvin for Machine Learning Using Python (CMLP Course)

Topics Covered:

• Introduction to basics of python
• Data structures, data manipulation also summarization in python
• Analytical modelling, statistical tests, and thus, logistic regression
• Clustering, decision trees as well as ensembles
• Association rule analysis as well as twitter analysis

https://youtu.be/qXyBFue_2Xg

Benefits of the Course:

• Ranked number 3 in the industry
• Furthermore, 100% practical medical learning training with Python
• Job support as well as alumni
• 1-year gold membership of AI as well as machine learning academy
• Lastly, 100% placement guarantee

Advantages and Disadvantages Of Machine Learning

Advantages Of Machine Learning:

1. Continuous improvement – as and when new data is provided, the efficiency of the model improves, with subsequent training.
2. Automation for everything – To summarize, it has the ability to automate various decision-making tasks, which frees a lot of time for the developers.
3. Trends and patterns identification – In contrast, various trends and patterns are identified with a huge amount of data.
4. Wide range of applications – As a result, it is used in various industries from defence to education.  Lastly, it is a branch of artificial intelligence, which is, one of the latest trends.

Disadvantages Of Machine Learning:

1. Data acquisition – Facing an imbalance in data collection from surveys, leads to poor accuracy in the models.  Thus, it’s a massive disadvantage.
2. Highly error prone – Furthermore, what we give the system is what it generates results.  So, when improper data is entered, the output will be error-prone.
3. Algorithm section – Indeed, running models with manual algorithms is a tedious task.
4. Time-consuming – Furthermore, time is taken to process a large amount of data.

To summarize– Machine Learning

Thus, machine learning is a booming career today. Furthermore, these 20 best learning books are a powerful tool to take you down this path.  Nonetheless, all industries have a growing demand for machine learning engineers today and along with this, you can take data science to further enhance your career.  This is because both these careers go hand-in-hand.  So get started and take the right step now.

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