In this chapter we will start by clarifying what Machine Learning is and why you may want to use it. Then, before we set out to explore the Machine Learning continent, we will take a look at the map and learn about the main regions and the most notable landmarks: supervised versus unsupervised learning, online versus batch learning, instance- based versus model-based learning.
Then we will look at the workflow of a typical ML project, discuss the main challenges you may face, and cover how to evaluate and fine-tune a Machine Learning system. This chapter introduces a lot of fundamental concepts and jargon that every data scientist should know by heart. It will be a high-level overview the only chapter without much code , all rather simple, but you should make sure everything is crystal-clear to you before continuing to the rest of the book.
If you are not sure, try to answer all the questions listed at the end of the chapter before moving on. What Is Machine Learning? Machine Learning is the science and art of programming computers so they can learn from data.
Here is a slightly more general definition: [Machine Learning is the] field of study that gives computers the ability to learn without being explicitly programmed. The examples that the system uses to learn are called the training set. Each training example is called a training instance or sample. In this case, the task T is to flag spam for new emails, the experience E is the training data, and the performance measure P needs to be defined; for example, you can use the ratio of correctly classified emails.
This particular performance measure is called accuracy and it is often used in classification tasks. If you just download a copy of Wikipedia, your computer has a lot more data, but it is not suddenly better at any task.
Thus, it is not Machine Learning. Why Use Machine Learning? First you would look at what spam typically looks like. You would write a detection algorithm for each of the patterns that you noticed, and your program would flag emails as spam if a number of these patterns are detected. You would test your program, and repeat steps 1 and 2 until it is good enough. Figure The program is much shorter, easier to maintain, and most likely more accurate.
Obviously this technique will not scale to thousands of words spoken by millions of very different people in noisy environments and in dozens of languages.
The best solution at least today is to write an algorithm that learns by itself, given many example recordings for each word. Finally, Machine Learning can help humans learn Figure : ML algorithms can be inspected to see what they have learned although for some algorithms this can be tricky. For instance, once the spam filter has been trained on enough spam, it can easily be inspected to reveal the list of words and combinations of words that it believes are the best predictors of spam.
Applying ML techniques to dig into large amounts of data can help discover patterns that were not immediately apparent. Https Www. Praveen Bodda. Popular in Technology. Catalin Cimpanu [ZDNet]. Anonymous 4DbsWj Anonymous igFZ18K.
Bobby Satheesan. Hakim Arif. Ky Ta. Gautham Puthran. Team Leader. Denis Jimenez. Saurabh Sahu. Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.
This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains.
You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. The book is quite popular among beginners because it nicely introduces all the machine learning algorithms through basic codes in Python and uses popular datasets to help newbies learn different methods the two libraries contain.
And after reading Hands-on Machine Learning, I must say that Geron does not disappoint, and the second edition is an excellent resource for Python machine learning. The book indeed has a more practical approach than the other academic books in machine learning out there. It starts with providing a basic layout of a sample machine learning project.
Next, it explains different machine learning algorithms like linear regression, logistic regression, Support Vector Machines, Decision Trees, etc. However, the journey towards landing your dream job in Machine Learning should not stop here. That can be considered a first step, but you need to put more effort into ace those demanding data science and machine learning interviews.
Move on to the next section of this blog that will complete your voyage of hands-on machine learning with Scikit-Learn and TensorFlow. We recommend the following Scikit-learn and TensorFlow Projects list as the final lap of mastering the two machine learning libraries. As the Scikit-learn library contains methods for utilising different machine learning algorithms, we will list our scikit-learn projects for you.
Here is a fun project to kick start your machine learning projects-based training journey primarily because it is related to human activities. Human beings tend to perform various tasks in a day, including eating, sleeping, talking, walking, etc. Objective: In this project, you will work on a multiclass classification problem. Dataset: The dataset contains information of 30 participants that were collected using embedded sensors Samsung Galaxy SII smartphone. The activities have been labelled as either of the following: Walking, Walking-upstairs, Walking-downstairs, Sitting, Standing, Laying.
Have you ever applied for a loan and wondered why the application got rejected even when you had a high CIBIL score? Well, there are many factors that affect the success of a loan application.
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