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Getting Started With Scikit-Learn: A Beginner'S Guide To Ml
Published 5/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 852.58 MB | Duration: 2h 48m
Foundations and Practical Applications
Published 5/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 852.58 MB | Duration: 2h 48m
Foundations and Practical Applications
What you'll learn
Fundamental concepts of Machine Learning and its various types.
Hands-on knowledge of various Machine Learning algorithms using the Scikit-Learn library.
Techniques to pre-process data, select the right model, train, test, and evaluate Machine Learning models.
Practical understanding of how to use Scikit-Learn for regression, classification, clustering, and dimensionality reduction tasks.
Model evaluation techniques and the understanding of underfitting and overfitting.
Requirements
Basic knowledge of Python programming is required as the course will be taught using Python.
Familiarity with basic mathematical concepts would be beneficial but not mandatory.
A computer with an Internet connection to download necessary libraries and datasets.
No prior knowledge of Machine Learning or Scikit-Learn is required.
Description
Welcome to the world of machine learning!Are you ready to unlock the potential of machine learning?This comprehensive course is designed to provide beginners with a solid foundation in machine learning using Scikit-Learn, one of the most popular and powerful machine learning libraries in Python. Whether you're a programming enthusiast, a data analyst, or a professional looking to expand your skill set, this course will equip you with the knowledge and practical skills to confidently dive into the world of machine learning.Throughout this course, you will learn the fundamental concepts and techniques of machine learning, including data preprocessing, model training, and evaluation. You will gain hands-on experience in building different machine learning algorithms, such as linear regression, logistic regression, decision trees, random forests, and K-nearest neighbors, to solve real-world problems. You will engage in practical exercises, quizzes and coding examples that allow you to implement machine learning algorithms using Scikit-Learn.By the end of this course, you will have a strong foundation in machine learning and the ability to apply Scikit-Learn effectively to solve various real-world problems. Whether you're looking to kickstart a career in data science or simply gain practical skills in machine learning, this course is the perfect starting point for your journey into the exciting field of machine learning with Scikit-Learn.
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: Simple Linear Regression
Lecture 2 Linear Regression using Scikit-learn
Section 3: Multiple Linear Regression
Lecture 3 Multiple Linear Regression in Scikit-learn
Section 4: Logistic Regression in Scikit-learn
Lecture 4 Logistic Regression
Section 5: K-Nearest Neighbors in Scikit-learn
Lecture 5 K-Nearest Neighbors
Section 6: Decision Tree in Scikit-learn
Lecture 6 Decision Tree
Section 7: Random Forest in Scikit-learn
Lecture 7 Random Forest
Beginners who are interested in Machine Learning and want to understand it through practical applications.,Python programmers who are interested in Machine Learning and want to learn how to implement Machine Learning algorithms using Scikit-Learn.,Data analysts or data scientists who want to upgrade their skills by learning Machine Learning techniques.,Anyone who is curious about how Machine Learning models work and how they can be implemented using Scikit-Learn.
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