Course HighlightsCOURSE
Machine Learning with Python: A Practical Introduction

Machine Learning with Python: A Practical Introduction

Learn the fundamentals of machine learning using Python. Discover how to uncover hidden insights, predict future trends, and create prototypes.

Develop these core skills and take a critical step forward in your data science career.

Machine Learning with Python: A Practical Introduction Highlights

  Course duration

Duration

  • 5 weeks
    at 4-6 hours/week
  Course Fee

Fee

US$ 99 - US$ 199

Course duration

Duration

  • 5 weeks
    at 4-6 hours/week
Course Fee

Fee

US$ 99 - US$ 199

The Python community has developed many features that assist programmers with machine learning implementation. As a language, Python's simplicity, consistency, platform freedom, flexibility and useful libraries has made it a very popular choice for machine learning for data science and AI.

In this course, you will learn about supervised vs. unsupervised learning. You will look into how statistical modeling relates to machine learning, and you will do a comparison of each. You will explore many popular algorithms, including classification, regression, clustering, and dimensional reduction. And you will investigate popular models such as train/test split, root mean squared error (RMSE), and random forests. You will look at real-life examples of machine learning and see how it affects society. Plus, you will discover how to transform your theoretical knowledge into a practical skill using hands-on labs.

Learning to analyze data with Python is a key skill for anyone who wants to excel in the field of data science. This course will provide you with an excellent foundation in using Python for machine learning, while also allowing you to take another step toward earning an IBM Data Science Professional Certificate.

This course comprises six purposely designed modules that take you on a carefully defined learning path. If you are thinking about taking the course separately, it is worth noting that it is part of the IBM Data Science Professional Certificate Program, and you may want to consider enrolling for the whole program rather than just enrolling for one course at a time.

It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules or submitting assignments. To give you an idea of how long the course takes to complete, it is anticipated that if you work 4-6 hours per week, you will complete the course in 5 weeks. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And don’t worry, you’re not alone! You will be encouraged to stay connected with your learning community and mentors through the course discussion space.

The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include discussion space, videos, reading material, quizzes, hands-on labs, quizzes, and final assignment.

Once you have successfully completed the course, you will earn your IBM Certificate.

As part of our mentoring service, you will have access to valuable guidance and support throughout the course. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues. Depending on the payment plan you have chosen, you may also have access to live classes and webinars, which are an excellent opportunity to discuss problems with your mentor and ask questions. Mentoring services may vary package wise.

You will:

  • Be familiar with machine learning terms, libraries, and the languages used to create them.
  • Be able to apply the appropriate form of regression to a data set for estimation.
  • Be able to apply an appropriate classification method for a particular machine learning challenge.
  • Be able to use the correct clustering algorithms on different data sets.
  • Be able to explain how recommendation systems work, and implement one on a data set.
  • Have demonstrated your understanding of machine learning in an assessed project.
  • Individuals looking to learn how to work with different kinds of data.
  • Individuals wanting to perform analysis on data.
  • Individuals wanting an introduction to Machine Learning with Python.

    There are no prerequisites for this course.

Course Outline

Course Overview
Pre-reqs
Changelog
Learning Objectives
Syllabus
Grading Scheme
Module Introduction
Learning Objectives
Welcome (3:20)
Introduction to Machine Learning (8:54)
Python for Machine Learning (6:16)
Supervised vs Unsupervised (6:04)
Module 1 - Graded Quiz
Module Introduction
Learning Objectives
Introduction to Regression (4:56)
Simple Linear Regression (12:56)
Model Evaluation in Regression Models (8:32)
Evaluation Metrics in Regression Models (3:11)
Lab: Simple Linear Regression (1hr)
Multiple Linear Regression (13.45)
Lab: Multiple Linear Regression (1hr)
Non-Linear Regression (7:40)
Lab: Polynomial Regression (1hr)
Lab: Non-linear Regression (1hr)
Module 2 - Graded Quiz
Module Introduction
Learning Objectives
Introduction to Classification (3:59)
K-Nearest Neighbors (9:17)
Evaluation Metrics in Classification (7:13)
Lab: KNN (45:00)
Introduction to Decision Trees (4:06)
Building Decision Trees (10:41)
Lab: Decision Trees (45:00)
Intro to Logistic Regression (8:00)
Logistic Regression vs Linear Regression (15:35)
Logistic Regression Training (13.55)
Lab: Logistic Regression (45:00)
Support Vector Machine (8:57)
Lab: SVM (Support Vector Machines) (45:00)
Module 3 - Graded Quiz
Module Introduction
Learning Objectives
Intro to Clustering (8:07)
Intro to k-Means (9:41)
More on k-Means (3:51)
Lab: k-Means (1hr)
Intro to Hierarchical Clustering (6:23)
More on Hierarchical Clustering (5:56)
Lab: Agglomerative Clustering (1hr)
DBSCAN (7:02)
Lab: DBSCAN Clustering (1hr)
Module 4 - Graded Quiz
Module Introduction
Learning Objectives
Intro to Recommender Systems (4:38)
Content-based Recommender Systems (5:17)
Lab: Content-based Recommendation Systems (1hr)
Collaborative Filtering (7:11)
Lab: Collaborative Filtering on Movies (1hr)
Module 5 - Graded Quiz
Download Your Certificate
Course Certificate

Earn your certificate

Once you have completed this course, you will earn your certificate.

Preview digital certificate
Machine Learning with Python: A Practical Introduction

FAQs

Yes. Machine Learning with Python: A Practical Introduction is 100% online. You will not be required to attend any classes in person. To enable this, however, you do need to have appropriate access to the internet to use the course materials. The materials for the course are in the form of articles, videos, and knowledge checks.

Machine Learning with Python: A Practical Introduction is a self-paced course. When you enroll, you will see in your dashboard that you have access to the module information and course materials from the start.

When you successfully complete this course, you will earn an IBM Certificate. Plus, you will be one step closer to earning IBM Professional Certification of you are taking it as part of the IBM Data Science Professional Certificate.  

Python is a high-level, open-source, programming language that offers an excellent approach to object-oriented programming. It is one of the most popular languages used by data scientists, and hence for machine learning. It is used on a variety of projects and applications. Python has a lot of features useful for dealing with arithmetic, statistics, and scientific functions, which makes it ideal for use in machine learning.

Python's popularity in scientific and research fields stems from its ease of use and straightforward syntax. This means it is simple to learn, even for individuals without an engineering or computing background. It's also excellent for rapid prototyping.