Fundamentals of Machine Learning using Python

Name: Fundamentals of Machine Learning using Python
Rating: 4.5 (8 reviews)

Proficiency in applying Machine Learning techniques using the scikit-learn Python library.

Created byLuca Berton

Last updated 10/2023

English

What you'll learn

Introduction to Machine Learning
Demystify Problem-Solving Steps
Machine Learning impact in industries
Machine Learning Python Libraries
Supervised vs. Unsupervised Learning

Course content

4 sections • 10 lectures • 1h 10m total length

Fundamentals of Machine Learning6:24
The "Fundamentals of Machine Learning with Python" course, led by instructor Luca Berton, offers a comprehensive exploration of machine learning, covering applications in various fields, a structured four-module curriculum with hands-on Python learning, and the promise of equipping learners with a deep understanding of key machine learning concepts, practical skills, and the ability to apply machine learning algorithms to real-world problems, making it an invaluable resource for those interested in data science and machine learning.

Introduction to Machine Learning7:55
Discover the power of Machine Learning in our comprehensive introduction. Explore real-world applications, techniques, and the difference between AI, Machine Learning, and Deep Learning. Dive into the future of technology with us!
The Power of Python and SciKit Learn in Machine Learning8:02
Unlock the potential of Python in data science and machine learning with essential libraries like NumPy, SciPy, Matplotlib, Pandas, and SciKit Learn. Dive into the world of data manipulation, scientific computation, visualization, and simplified machine learning. Explore the capabilities that make Python the top choice for data scientists.
Understanding Supervised and Unsupervised Learning in Machine Learning7:27
Explore the essence of machine learning with an in-depth look at supervised and unsupervised learning. Discover how labeled data guides supervised learning, while unsupervised learning autonomously uncovers hidden insights. Understand the key differences and the diverse techniques that make these approaches fundamental in the realm of data science and machine learning.

A Comprehensive Introduction to Regression Analysis7:29
Discover the fundamentals of regression analysis, a powerful statistical technique used to predict continuous values. Explore how regression models establish relationships between variables, making it invaluable in various fields, from sales forecasting to real estate pricing. Unveil the versatility and importance of regression in data-driven decision-making.
A Beginner's Guide to Linear Regression6:46
Learn the fundamentals of linear regression in this comprehensive video tutorial. Understand how to use this essential machine learning technique to make predictions effectively.
Mastering Evaluation Metrics in Regression Models5:58
Explore the significance of evaluation metrics in the realm of data science and predictive modeling. Learn about key metrics, including Mean Absolute Error, Mean Squared Error, and more, for assessing and fine-tuning your regression models.
Understanding Model Evaluation in Regression Models6:08
Enhance your understanding of model evaluation in regression with this comprehensive guide. Learn how to select the most suitable evaluation approach and metrics for accurate predictive models.
Mastering Simple Linear Regression with scikit-learn: A Step-by-Step Guide10:31
In this comprehensive guide, learn how to harness the potential of simple linear regression using scikit-learn. Explore the step-by-step process of creating, training, and evaluating a linear regression model for data analysis and prediction. Master the fundamentals of linear regression with practical examples and insights.

Requirements

Python Programming

Description

In today's rapidly evolving technological landscape, Machine Learning has emerged as a transformative force across many industries. This introductory course offers a deep dive into the practical applications of machine learning in fields such as healthcare, banking, telecommunications, and more. With an emphasis on real-world relevance, this course equips you with the knowledge and skills to harness the power of Machine Learning to solve complex problems and drive innovation in your chosen industry.

Throughout the course, you will embark on a journey that begins with a solid foundation in Machine Learning fundamentals. You will explore the distinction between supervised and unsupervised learning, gaining a nuanced understanding of when and how to apply each approach. Moreover, you will delve into the vast landscape of Machine Learning algorithms, learning their strengths, weaknesses, and real-world use cases.

One of the course's central pillars is the practical implementation of Machine Learning models using Python libraries. Python's versatility and a rich ecosystem of libraries such as scikit-learn, TensorFlow, and Keras provide you with the tools to build, train, and evaluate models effectively. You will gain hands-on experience in applying these libraries to real-world datasets, honing your ability to transform data into actionable insights.

By the end of this course, you will not only possess a strong theoretical foundation in Machine Learning but also the practical skills needed to leverage this transformative technology in various professional domains. Whether you are a healthcare professional seeking to enhance patient care, a banker aiming to optimize financial decisions, or a telecommunications expert looking to improve network efficiency, this course empowers you to make data-driven decisions that drive innovation and success in your industry.

Join us on this exciting journey into Applied Machine Learning and unlock new possibilities for problem-solving and innovation in your field.

Who this course is for:

Data Scientists and Analysts
Software Developers
Business Analysts
Researchers and Academics
Entrepreneurs and Innovators
Professionals in Specific Industries
Data Enthusiasts
Anyone Curious About Machine Learning
Beginner of Python curious about data science

Fundamentals of Machine Learning using Python

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 6min

Module 1: Introduction and Regression3 lectures • 23min

Module 2: Classification5 lectures • 37min

Thank You1 lecture • 4min

Requirements

Description

Who this course is for: