Machine Learning tutorial with Python is designed for those who are new to data analysis and want to learn how to build models using Python, one of the most popular programming languages for this field.
We will start by setting up our development environment, which includes installing Python and the necessary libraries such as NumPy, Pandas and scikit-learn. We will learn about the basic concepts of data analysis and the different types of models, such as supervised learning, unsupervised learning, and reinforcement learning.
Next, we will explore the process of machine learning by building models, including data preparation, feature selection, model selection, and model evaluation. We will learn about different types of algorithms such as linear regression, logistic regression, decision trees, and random forests. We will also learn how to use scikit-learn, a popular data analysis library in Python, to build and evaluate models.
After learning the basics of data analysis, we will dive into more advanced topics such as deep learning, natural language processing, and computer vision. We will learn how to use popular deep learning libraries like TensorFlow and Keras to build models for image and text data.
Once we have a solid understanding of data analysis, we will put our knowledge to the test by building a simple project. We will create a project that uses data analysis to predict a target variable. This will give us hands-on experience in using Python and data analysis libraries to build and evaluate models.
Finally, we will learn how to deploy our models to a production environment, which includes configuring a web server and making our models accessible to users.
By the end of this tutorial, you will have a solid understanding of data analysis and the skills to build your own models using Python.
In summary, this tutorial covers all the basics of
Author: Dhaval Patel