Machine Learning Step by Step Guide To Implement Machine Learning Algorithms with Python by Rudolph Russell | PDF Free Download.
Machine Learning Contents
Chapter 1 Introduction To Machine Learning
- What is machine learning?
- Why machine learning?
- When should you use machine learning?
- Types of Systems of Machine Learning
- Supervised and unsupervised learning
- Supervised Learning
- The most important supervised algorithms
- Unsupervised Learning
- The most important unsupervised algorithms
- Reinforcement Learning
- Batch Learning
- Online Learning
- Instance-based learning
- Model-based learning
- Bad and Insufficient Quantity of Training Data
- Poor-Quality Data
- Irrelevant Features
- Feature Engineering
- Overfitting the Data
- Underfitting the Data
Chapter 2 Classification
- The MNIST
- Measures of Performance
- Confusion Matrix
- Recall Tradeoff
- Multi-class Classification
- Training a Random Forest Classifier
- Error Analysis
- Multi-label Classifications
- Multi-output Classification
Chapter 3 How To Train A Model
- Linear Regression
- Computational Complexity
- Gradient Descent
- Batch Gradient Descent
- Stochastic Gradient Descent
- Mini-Batch Gradient Descent
- Polynomial Regression
- Learning Curves
- Regularized Linear Models
- Ridge Regression
- Lasso Regression
Chapter 4 Different models combinations
- Implementing a simple majority classifier
- Combining different algorithms for classification with a majority vote
Introduction to Machine Learning Step by Step
If I ask you about “Machine learning,” you'll probably imagine a robot or something like the Terminator. In reality t, machine learning is involved not only in robotics but also in many other applications.
You can also imagine something like a spam filter as being one of the first applications in machine learning, which helps improve the lives of millions of people. In this chapter, I'll introduce to you what machine learning is, and how it works.
What is machine learning?
Machine learning is the practice of programming computers to learn from data. In the above example, the program will easily be able to determine if given are important or are “spam”. In machine learning, data referred to as called training sets or examples.
Why machine learning?
Let’s assume that you'd like to write the filter program without using machine learning methods. In this case, you would have to carry out the following steps:
- In the beginning, you'd take a look at what spam e-mails look like. You might select them for the words or phrases they use, like “debit card,” “free,” and so on, and also from patterns that are used in the sender’s name or in the body of the email
- Second, you'd write an algorithm to detect the patterns that you've seen, and then the software would flag emails as spam if a certain number of those patterns are detected.
- Finally, you'd test the program, and then redo the first two steps again until the results are good enough.
Because the program is not software, it contains a very long list of rules that are difficult to maintain. But if you developed the same software using ML, you'll be able to maintain it properly
In addition, the email senders can change their e-mail templates so that a word like “4U” is now “for you,” since their emails have been determined to be spam.
The program using traditional techniques would need to be updated, which means that, if there were any other changes, you would l need to update your code again and again and again.
On the other hand, a program that uses ML techniques will automatically detect this change by users, and it starts to flag them without you manually telling it to.
Also, we can use, machine learning to solve problems that are very complex for non-machine learning software. For example, speech recognition: when you say “one” or “two”, the program should be able to distinguish the difference.
So, for this task, you'll need to develop an algorithm that measures sound. In the end, machine learning will help us to learn, and machine-learning algorithms can help us see what we have learned.
When should you use machine learning?
- When you have a problem that requires many long lists of rules to find the solution. In this case, machine-learning techniques can simplify your code and improve performance.
- Very complex problems for which there is no solution with a traditional approach.
- Non- stable environments’: machine-learning software can adapt to new data.
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