Williams - Machine Learning with Python

Machine Learning with Python

The Ultimate Beginners Guide to Learn Machine Learning with Python Step by Step

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Table of Contents

Machine Learning is the science of extracting meaningful information from data. It is multidisciplinary science involving several disciplines like statistics, artificial intelligence, and computer science. Furthermore, it has broad applications in everyday life. From recommendations of what product to purchase, to what movie or tv series to watch. Many websites and social media sites also use complex machine learning algorithms for their operations.

Machine learning methods are classified into a number of categories. This classification depends on a number of factors including whether they can be trained by humans or not, if they can learn incrementally, or if they can determine patterns in data and build models.

In the supervised machine learning methods, the algorithm finds the desired output based on the input provided by the user. In other words, such methods can automate decision-making processes from generalizing known examples. Identifying handwritten digits is a famous example of supervised machine learning algorithms. In this example the scan of handwriting is the input and the actual digits are desired outputs. Another example is weather forecasting based on historical data. The most famous supervised algorithms are as follows:

- k-nears neighbors

- Linear regression

- Neural networks

- Support vector machines

- Logistic regression

- Decision trees and random forests

In these types of machine learning algorithms, only the input is known, whereas no output data is provided. An example of an unsupervised learning algorithm is filtering spam mails using clustering. Another example is identifying. The most famous unsupervised algorithms are as follows:

- k -means clustering

- Hierarchical clustering

- Principal component analysis

Reinforcement learning is a type of machine learning methods in which suitable actions should be taken to maximize reward in a particular situation. Such algorithms are employed by many software and machines to determine the best possible behavior in a specific situation. Reinforcement learning algorithms can be applied in various fields such as industrial automation, data processing, and creating training systems.

Python is a powerful programming language developed by Guido Van Rossum in the late 1989s. This language is currently used in many domains such as web development, software development, education, and data analysis. Reasons for its widespread use include:

- Python is easy to learn and understand

- The Syntax of python is easy

- Python has its own way of managing memory associated with objects.

- Python is not propriety software.

- It is compatible with all platforms (i.e. Windows, Linux, and Mac)

- Can be interfaced with other programming languages

- Many python library and packages are available for data science and machine learning related applications.

Anaconda is a free and open-source distribution of the Python programming language for scientific computing as well as machine-learning related applications. It simplifies package management and deployment. Currently, the Anaconda distribution is used by over 13 million people and includes more than 1,400 popular data science packages. To install the Anaconda distribution on Windows follow the bellowing steps:

Step 1: Download the latest Anaconda distribution file from https://www.anaconda.com/distribution/

There are download options for 32-bit and 64-bit operating systems (Figure 1.1).

Step 2: Click on the executable installation file. A welcome screen will appear (Figure

1.2)

Step 3: Click next, read and accept the license agreement by clicking on I Agree button (Figure

1.3)

Step 4: Choose installation type (i.e. One user or All users) (Figure 1.4)

Step 5: Choose a location to install Anaconda Python distribution (Figure 1.5)

Step 6: Select all options in Advanced installation options screen and ignore all warnings

(Figure 1.6) and click on the Install button.

Step 7: Wait until the installation is complete (Figure 1.7) and then click next button

Step 8: Now the Anaconda python distribution has been successfully installed, click on the

Finish button (Figure 1.8)

Step 9: To check whether the Anaconda has been installed successfully, on command prompt type python, you should see python shell as shown in Figure 1.9

Figure 1.1 Anaconda distribution website

Figure 1.2 Anaconda installation welcome screen

Figure 1.3 License Agreement screen

Figure 1.4 Installation type screen