Python Machine Learning Crash Course for Beginners

Description

The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.

Machine learning is a field of computer science through which you can create complex models that perform multiple functions using mathematical input. Python is a popular choice to create machine learning models due to a plethora of libraries easily accessible. This course takes you through this impressive combination of Python and machine learning, teaching you the basics of machine learning to create your own projects. You'll begin learning about different types of machine learning models and how to choose the relevant ones for your project. You'll learn to optimize this model and apply performance metrics to track its performance. You'll also learn topics like regression, classification, and clustering to improve the performance of your model. You'll learn the basics of neural networks and use scikit-learn to perform calculations in your project. By the end of this course, you'll have created a face recognition application using everything you've learned in this course. The code bundle for this course is available at https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners

Train different types of machine learning models for your project
Prepare and clean data for your project
Optimize your machine learning model to best suit your project needs
Build your own machine learning model from scratch
Apply performance metrics to track the performance of your model
Use scikit-learn to perform calculations in your project

This course is for Python developers who are new in the field of machine learning. No prior knowledge or experience of machine learning is required. A basic understanding of Python programming will be needed here.

This is a hands-on course that takes you through all the machine learning concepts step-by-step while showing you real-world applications for each step.

Build a face recognition application from scratch * Prepare and train your data for your projects * Learn real-world applications of your algorithms

https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners

AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.

1. Introduction to the Course

2. Why Machine Learning

3. Process of Learning from Data

4. Machine Learning Methods

5. Data Preparation and Preprocessing

6. Machine Learning Models and Optimization

7. Building Machine Learning Model from Scratch

8. Overfitting, Underfitting and Generalization

9. Machine Learning Model Performance Metrics

10. Dimensionality Reduction

11. Deep Learning Overview

12. Hands-on Machine Learning Project Using Scikit-Learn

13. OPTIONAL Section- Mathematics Wrap-up