Machine learning (ML) is a powerful tool that allows computers to learn from data without explicit programming. It has a wide range of applications, from spam filtering to medical diagnosis. Python is a versatile programming language that is well-suited for ML, thanks to its extensive libraries and ease of use.

This guide will provide you with a comprehensive overview of ML with Python. We will cover everything from data preprocessing to model evaluation, with practical examples and expert tips. By the end of this guide, you will be able to use ML to solve real-world problems.

Before we get started, you will need to have a basic understanding of Python. You should also be familiar with linear algebra and statistics. If you are not familiar with these topics, there are many resources available online that can help you get up to speed.

Hacker's Guide to Machine Learning with Python: Hands-on guide to solving real-world Machine Learning problems with Deep Neural Networks using Scikit-Learn, TensorFlow 2, and Keras

by Venelin Valkov

4.7 out of 5

Language	:	English
File size	:	34515 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	295 pages
Lending	:	Enabled
Screen Reader	:	Supported

FREE

The first step in any ML project is data preprocessing. This involves cleaning the data, removing outliers, and scaling the features. Data preprocessing is important because it can improve the performance of your ML models.

There are many different techniques that you can use for data preprocessing. The most common techniques include:

• Data cleaning: This involves removing any errors or inconsistencies from the data.
• Outlier removal: This involves removing any data points that are significantly different from the rest of the data.
• Feature scaling: This involves scaling the features of the data so that they are all on the same scale.

You can use the following Python code to clean your data:

python import pandas as pd

Read the data from a CSV file

data = pd.read_csv('data.csv')

Remove any rows with missing values

data.dropna(inplace=True)

Remove any outliers

data = data[(data['feature1'] > -3) & (data['feature1'] Model Selection

Once you have preprocessed your data, you need to select a ML model. There are many different ML models available, each with its own strengths and weaknesses. The best model for your project will depend on the specific problem that you are trying to solve.

The most common ML models include:

• Linear regression: This model is used to predict a continuous variable from one or more independent variables.
• Logistic regression: This model is used to predict a binary outcome from one or more independent variables.
• Decision trees: This model is used to classify data into different categories.
• Support vector machines: This model is used to classify data into different categories or to regress a continuous variable.
• Neural networks: This model is used to solve a wide range of problems, including image recognition, natural language processing, and speech recognition.

You can use the following Python code to train a linear regression model:

Once you have trained your model, you need to evaluate its performance. There are many different metrics that you can use to evaluate a ML model, including:

• Accuracy: This metric measures the percentage of correct predictions that the model makes.
• Precision: This metric measures the percentage of positive predictions that are correct.
• Recall: This metric measures the percentage of actual positives that are correctly predicted.
• F1-score: This metric is a weighted average of precision and recall.

You can use the following Python code to evaluate a linear regression model:

This guide has provided you with a comprehensive overview of ML with Python. We have covered everything from data preprocessing to model evaluation, with practical examples and expert tips. By the end of this guide, you should be able to use ML to solve real-world problems.

I encourage you to continue learning about ML. There are many resources available online that can help you get started. You can also find many ML communities online where you can ask questions and share ideas.

Happy hacking!

Hacker's Guide to Machine Learning with Python: Hands-on guide to solving real-world Machine Learning problems with Deep Neural Networks using Scikit-Learn, TensorFlow 2, and Keras

by Venelin Valkov

4.7 out of 5

Language	:	English
File size	:	34515 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	295 pages
Lending	:	Enabled
Screen Reader	:	Supported

FREE