Explore the world of Machine Learning (ML) with Python! This course is perfect whether you are looking to kickstart your journey into Machine Learning and Deep Learning or take your Data Science career to the next level.

Course Overview

In this comprehensive course, you'll dive into the core concepts of machine learning using Python, a widely used programming language. The course covers the distinction between supervised and unsupervised learning and examines the relationship between statistical modeling and machine learning.

You will explore popular algorithms, including Classification, Regression, Clustering, and Dimensional Reduction, along with essential models like Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests. Through practical, real-world examples, you'll see the societal impact of machine learning in ways you might not expect.

Throughout the course, hands-on labs in Python will enable you to transform your theoretical knowledge into practical skills, applying machine learning techniques to solve problems. You'll gain confidence in using key algorithms and models, preparing you to apply machine learning in real-world scenarios.

Enroll today and kickstart your data science career... You have a lot to look forward to!

IBM Data Science Professional Certificate

This course is part of the IBM Data Science Professional Certificate. If you’re keen to kickstart a career in data scientist, we recommend you enroll for the full Professional Certificate program and work through the courses in order. Within just a few months, you’ll have job-ready skills and practical experience on your resume that will catch the eye of an employer!

Course Syllabus

Module 1 - Introduction to Machine Learning

Introduction to Machine Learning
Python for Machine Learning
Supervised vs Unsupervised Learning

Module 2 - Regression

Introduction to Regression
Simple Linear Regression
Model Evaluation in Regression Models
Evaluation Metrics in Regression Models
Multiple Linear Regression
Non-linear Regression

Module 3 - Classification

Introduction to Classification
K-Nearest Neighbors
Evaluation Metrics in Classification
Introduction to Decision Trees
Building Decision Trees
Introduction to Logistic Regression
Logistic Regression vs Linear Regression
Logistic Regression Training
Support Vector Machines

Module 4 - Clustering

Introduction to Clustering
K-Means Clustering
Hierarchical Clustering
Density-Based Clustering

Module 5 – Recommender Systems

Introduction to Recommender Systems
Content-based recommender systems
Collaborative Filtering

Module 6– Final Project

What You'll Learn

Explain the difference between the two main types of machine learning methods: supervised and unsupervised
Describe Supervised learning algorithms, including classification and regression
Describe Unsupervised learning algorithms, including Clustering and Dimensionality Reduction
Explain how statistical modelling relates to machine learning and how to compare them
Discuss real-life examples of the different ways machine learning affects society
Build a prediction model using classification

Recommended Skills Before Taking this Course

A basic understanding of Python, along with knowledge of data analysis and visualization techniques, is required. Additionally, a minimum proficiency in high school-level mathematics is needed.

Machine Learning Fundamentals with Python

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