"Python Machine Learning"

Book for Everyone

So, you've decided to learn about Machine Learning, but you hate to keep digging numerous resources? You're not alone. That's why we've created this free book to teach you the Machine Learning and Deep Learning core concepts, all in one place, for non-experts that are interested in having a great start.

  • Discover what is Machine Learning and what makes it so powerful. You will also learn what are different types of Machine Learning.
  • Simple, comprehensive tutorials explaining Machine Learning core algorithms!
  • No Machine learning background is required! Even if you never heard of Machine Learning so far! This book is created for everybody!

Sample Chapters

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Linear regression - Linear Regression is a technique used to analyze a linear relationship between input variables and a single output variable.

  • Applications
  • Cost functions detailed explanation
  • Code examples

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Support Vector Machine - A Support Vector Machine (SVM for short) is a powerful Machine Learning algorithm that is used to classify data. 

  • Support Vectors definition
  • Describe different types of SVMs
  • Working example

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Principal Component Analysis - Principal Component Analysis (PCA) is an extremely useful Unsupervised Learning technique for deriving an overall, linearly independent, trend for a given dataset with many variables.

  • The core concept
  • Applications
  • Code Example

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Convolutional Neural Networks (CNNs) - One the most famous Deep Learning architecture, particularly famous due to its superiority in Computer Vision application.

  • Overview of Deep Learning
  • Detailed explanation
  • Illustrative examples

What makes Machine Learning so POWERFUL and what are the setbacks

It learns from data just as human, but with much more computational power and of course less intelligently at this moment...

Pros

  • It recognizes pattern from a massive data source.
  • Human can catch a break due to automation.
  • Simplicity compared to traditional algorithms. Usually all you need is a data representation.
  • It is easy to engineer. Usually we are not dealing with sophisticated mathematical models or system architecture to train and run and Machine Learning model.
  • It demonstrated superior performance compared to tradition models and even human! Isn't it enough for something to pique our interest?

Cons

  • Data hunger. We usually need a lot of data which might be hard to collect and process.
  • Biased results due to the data variation. Machine Learning models are susceptible to error. The model might be biased to a portion of the data for a wide variety of known of unknown reasons.  
  • Privacy issues. As Machine Learning learns from data, the sensitive information might be revealed by the Machine Learning model to the unwanted parties

Fundamental Questions

How was the advent and evolution of Machine Learning?

You can argue that the start of modern Machine Learning comes from Alan Turing’s “Turing Test” of 1950. The TuringTest aimed to find out if a computer is brilliant (or at least smart enough to fool a human into thinking it is). Machine Learning continued to develop with game playing computers. The games these computers play have grown more complicated over the years from checkers to chess to Go. Machine Learning was also used to model pattern recognition systems in nature such as neural networks. But Machine Learning didn’t just stay confined to large computers stuck in rooms. Robots were designed that could use Machine Learning to navigate around obstacles automatically. We continue to see this concept in the self-driving cars of today. Machine Learning eventually began to be used to analyze large sets of data to conclude. This allowed for humans to be able to digest large, complex systems through the use of Machine Learning. This was an advantageous result for those involved in marketing and advertisement as well as those concerned with complex data. Machine Learning was also used for image and video recognition. Machine Learning allowed for the classification of objects in pictures and videos as well as identification of specific landmarks of interest. Machine Learning tools are now available through the Cloud and on large scale distributed systems.

Why is machine learning so important?

Machine learning has practical applications for a range of common business problems. By using machine learning, organizations can complete tasks in less time and more efficiently. One example could be preprocessing a set of data for a future stage that requires human intervention. Tasks that would have previously required lots of user input can now be automated to some degree. The saved resources can then be put towards something else that needs to be done. Beyond task automation, machine learning can be used to analyze large quantities of complex data to make predictions. Data analysis is an essential task for many businesses. For example, a company could analyze sales data to find out where profitable opportunities are or to find out where it risks losing money. Using machine learning can potentially allow for real-time analysis of complex data. Such an ability might be required for mission-critical systems. Machine Learning is also an important topic for research and continued development. Currently, machine learning still has a lot of limitations and isn’t close to replacing the need for a live person. Machine learning’s constant evolution could offer solutions for hard problems that might take up too many resources now to even consider.

Who is using ML and why (government, healthcare system, etc.)?

Machine Learning stands to impact most industries in some way so many managers and higher-ups are trying to at least learn what it is if not what it can do for them. Machine Learning models are expected to get better at prediction when supplied with more information. Nowadays, it is effortless to obtain large amounts of information that can be used to train very accurate models. The computers of today are also stronger than those available in the past and offer options such as cloud solutions and distributed processing to tackle hard Machine Learning problems. Many of these options are readily available to almost anyone can use Machine Learning . We can see examples of Machine Learning in self-driving cars, recommendation systems, linguistic analysis, credit scoring, and security to name a few. Financial services can use Machine Learning to provide insights about client data and to predict areas of risk. Government agencies with access to large quantities of data and an interest in streamlining or at least speeding up parts of their services can utilize Machine Learning . Health care providers with cabinets full of patient data can use Machine Learning to aid in diagnosis as well as identifying health risks. Shopping services can use customers’ purchase histories and Machine Learning techniques to make personalized recommendations and gauge dangerous products. Anyone with a large amount of data stands to profit from using Machine Learning .