If you are someone who is looking to learn Machine learning and have a passionate desire and guts to go deeper in it then the Machine Learning for Dummies is your key to absorb almost everything related to ML.
In all aspects, Machine Learning for Dummies is good except its title, in the beginning, I was very unhappy with it, no one likes if some call them dumb, but after reading it, I simply ignore it.
Machine Learning for Dummies covers almost everything about ML and build a good understanding of this subject. It is not an advanced level book but yes if a beginner wants to read it he will never get disappointed.
And also if you are someone who doesn’t even code ever in your life and just starting, still machine learning for dummies will help you allot with machine learning, the only thing is that you can’t build something with this its practical guide.
Machine Learning for Dummies is divided into six parts. If you are interested in it, you can download it from the link at bottom of this article for absolutely free.
In supervised learning, the machine is taught by examples, whereas in unsupervised learning the machine study data to identify patterns, there are only input variables (X) but no corresponding output variables.
If we talk about reinforcement learning, the machine is provided with a set of actions, parameters and end values. If you want to know more about ML algorithms that divided into these three types, feel free to check out below article.
Machine Learning For Dummies explains how to get started, provides detailed discussions of how the underlying algorithms work, uses languages such as Python and R to make machine learning possible specifies how to do practical things using.
Machine Learning for Dummies also covers lots of other concepts of ML such that statistics, linear models, demystifying the math, leveraging similarity, neural networks, complexity with neural networks.
The writers also talk about support vector machines, big data and much more in this “Machine Learning For Dummies”.
Machine Learning for Dummies talks in detail about the programming languages and tools integral to machine learning, most of the codes are written in Machine Learning for Dummies are in Python and R.
In Machine Learning For Dummies, there is not so much coding, and whatever the coding is there it is in Python and R the writer has used to teach machines to find patterns and analyze results.
The knowledge in this book flows in plain and simple English.
Machine Learning for Dummies is written by John Paul Mueller and Luca Massaron. John Paul Mueller is a well-known author, he has already written 108 books and more than 600 articles, whereas Luca Massaron is a data scientist and a specialist in multivariate statistical analysis and machine learning.
Table of content of Machine Learning for Dummies:
Part 1: Introducing How Machines Learning 7
CHAPTER 1: Getting the Real Story about AI 9
CHAPTER 2: Learning in the Age of Big Data 23
CHAPTER 3: Having a Glance at the Future 35
Part 2: Preparing Your Learning Tools 45
CHAPTER 4: Installing an R Distribution 47
CHAPTER 5: Coding in R Using RStudio 63
CHAPTER 6: Installing a Python Distribution 89
CHAPTER 7: Coding in Python Using Anaconda 109
CHAPTER 8: Exploring Other Machine Learning Tools 137
Part 3: Getting Started with the Math Basics 145
CHAPTER 9: Demystifying the Math Behind Machine Learning 147
CHAPTER 10: Descending the Right Curve 167
CHAPTER 11: Validating Machine Learning 181
CHAPTER 12: Starting with Simple Learners 199
Part 4: Learning from Smart and Big Data 217
CHAPTER 13: Preprocessing Data 219
CHAPTER 14: Leveraging Similarity 237
CHAPTER 15: Working with Linear Models the Easy Way 257
CHAPTER 16: Hitting Complexity with Neural Networks 279
CHAPTER 17: Going a Step beyond Using Support Vector Machines 297
CHAPTER 18: Resorting to Ensembles of Learners 315
Part 5: Applying Learning to Real Problems 331
CHAPTER 19: Classifying Images 333
CHAPTER 20: Scoring Opinions and Sentiments 349
CHAPTER 21: Recommending Products and Movies 369
Part 6: The Part of Tens 383
CHAPTER 22: Ten Machine Learning Packages to Master 385
CHAPTER 23: Ten Ways to Improve Your Machine Learning Models 391
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