Python Machine Learning Crash Course for Beginners
Dive into the world of machine learning and create projects with real-world applications
Updated on Apr, 2024
Language - English
Development,Data Science,Machine Learning
Lectures -49
Duration -9 hours
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Course Description
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.
Goals
What will you learn in this course:
- 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
Prerequisites
What are the prerequisites for this course?
- No prior knowledge or experience of machine learning is required. A basic understanding of Python programming will be needed here.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction to the Course
2 Lectures
-
Introduction
03:15
03:15
-
Focus of the Course
09:26
09:26
Why Machine Learning
3 Lectures
Process of Learning from Data
2 Lectures
Machine Learning Models
8 Lectures
Data Preparation and Preprocessing
5 Lectures
Machine Learning Models and Optimization
5 Lectures
Building a Machine Learning Model from Scratch
6 Lectures
Overfitting, Underfitting, and Generalization
6 Lectures
Machine Learning Model Performance Metrics
2 Lectures
Dimensionality Reduction
2 Lectures
Deep Learning Overview
3 Lectures
Hands-On Machine Learning Project Using Scikit-Learn
4 Lectures
Mathematics Wrap-Up
1 Lectures
Instructor Details
Packt Publishing
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