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Machine Learning with Python

Approx. 21 hours to complete

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Course Summary

Learn how to use Python for machine learning in this hands-on course. Gain practical skills in data preprocessing, modeling, and evaluation, and apply them to real-world problems.

Key Learning Points

Learn the basics of Python programming language and its application in machine learning
Explore data preprocessing, model selection, and evaluation techniques
Apply your skills to real-world problems and datasets

Job Positions & Salaries of people who have taken this course might have

- USA: $112,000
- India: ₹1,100,000
- Spain: €45,000
- USA: $112,000
- India: ₹1,100,000
- Spain: €45,000
- USA: $96,000
- India: ₹900,000
- Spain: €38,000
- USA: $112,000
- India: ₹1,100,000
- Spain: €45,000
- USA: $96,000
- India: ₹900,000
- Spain: €38,000
- USA: $129,000
- India: ₹1,200,000
- Spain: €50,000

Learning Outcomes

Develop practical skills in Python programming for machine learning
Understand data preprocessing and model selection techniques
Apply machine learning to real-world problems and datasets

Prerequisites or good to have knowledge before taking this course

Basic understanding of Python programming
Familiarity with linear algebra and calculus

Course Difficulty Level

Intermediate

Course Format

Online self-paced course
Hands-on exercises and projects
Video lectures and quizzes

Similar Courses

Applied Data Science with Python
Data Science Essentials
Applied Machine Learning

Related Education Paths

Related Books

Description

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.

In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you’ll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. 3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course.

Outline

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

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

Classification
Introduction to Classification
K-Nearest Neighbours
Evaluation Metrics in Classification
Introduction to Decision Trees
Building Decision Trees
Intro to Logistic Regression
Logistic regression vs Linear regression
Logistic Regression Training
Support Vector Machine
Classification

Clustering
Intro to Clustering
Intro to k-Means
More on k-Means
Intro to Hierarchical Clustering
More on Hierarchical Clustering
DBSCAN
Clustering

Recommender Systems
Intro to Recommender Systems
Content-based Recommender Systems
Collaborative Filtering
Recommender System

Final Project
OPTIONAL: Signing-up for a Watson Studio Account
OPTIONAL: Sharing Notebooks on Watson Studio
Congratulations!
IBM Digital Badge
Final Exam

Summary of User Reviews

Discover the power of machine learning with Python in this comprehensive course. Students rave about the thorough explanations and hands-on practice. One key aspect that many users thought was good is the instructor's ability to break down complex topics and make them easy to understand. However, some users have mentioned a few cons, such as the course can be too basic for those with prior experience in machine learning and there are occasional technical glitches.

Key Aspect Users Liked About This Course

The instructor's ability to break down complex topics and make them easy to understand

Pros from User Reviews

Thorough explanations and hands-on practice
Great for beginners
Instructor breaks down complex topics well

Cons from User Reviews

Can be too basic for those with prior experience in machine learning
Occasional technical glitches
Some lectures can be lengthy and repetitive

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