Course HighlightsCOURSE
 Machine Learning with Python

Machine Learning with Python

Explore 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 Highlights

  Course duration

Duration

  • 3 weeks, online
    1 hour/week
  Course Fee

Fee

US$ 99 - US$ 199

Course duration

Duration

  • 3 weeks, online
    1 hour/week
Course Fee

Fee

US$ 99 - US$ 199

This Machine Learning with Python course dives into the basics of machine learning using an approachable and well-known programming language. You'll learn about supervised vs unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each. Plus, you will look at real-life examples of machine learning and how it impacts and helps businesses and society.
 
In this course, you will explore many algorithms and models, including classification, regression, clustering, and dimensional reduction. You will also build your understanding of popular models such as train/test split, root mean wquared error, and random forests.
 
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 and set you up for the next step in your data science career.
This IBM certified course comprises five purposely designed modules that take you on a carefully defined learning journey.
 
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 1 hour per week, you will complete the course in 3 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 videos, reading material, and online exams questions.
 
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 will vary across packages.
 
Once you have successfully completed the course, you will earn your Certificate of Completion.

After this course you will understand:

  • Statistical modeling.
  • Real-life examples of machine learning.
  • Python libraries.
  • Popular algorithms: regression, classification, and clustering.
  • Recommender systems: content-based and collaborative filtering.
  • Popular models: train/test split, gradient descent, and mean squared error.
     
  • 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.

Ideally you should have completed the following courses:

  • Python for Data Science
  • Data Science Hands-on with Open Source Tools
  • Data Analysis with Python

If you have not taken these courses, you need to have experience of using Python for data science and data analysis, plus some experience using open source tools.

Course Outline

Course Certificate

Earn your certificate

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

Preview digital certificate
 Machine Learning with Python

FAQs

Python is a high-level, open-source programming language that provides an easy-to-use approach to object-oriented programming. It is one of the most widely used languages for machine learning, as well as data science and AI as a whole. It is employed in many different projects and applications. However, Python is often the go-to language to use for machine learning because it contains many features that are helpful for working with statistics, and scientific functions. 

Python's prominence in the scientific and research disciplines is due to its simple syntax and ease of usage. It is easy to understand, especially for those without a background in engineering or computing, and it’s also popular for quick prototyping. 

 

This is a hot subject for debate in the world of data science and AI. IBM considers the topic very well in a page dedicated to the subject (see link below). From IBM’s perspective, both languages are valuable for data science and AI, and each brings its own strengths and weaknesses. Both languages are popular in data science because they each work well for many data science tasks. These can range from data manipulation to big data exploration. Their differences can be best understood, therefore, through consideration of how each one has come into existence. Python came into being around 1989 and is considered a general-purpose programming language. However, R has grown directly from statistical analysis, and is therefore extremely powerful but more complex to use.  

For more information on this, we recommend you read this article written by IBM:  www.ibm.com/cloud/blog/python-vs-r 

Yes, this is a mentored course. As part of SkillUp Online’s 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. Plus, you will have the opportunity to discuss problems and ask questions in one-to-one sessions with your mentor.  

Yes, you will be issued an IBM Professional Certificate when you complete this course. You can upload it to your LinkedIn profile and add it to your resumé.  

This course is fully online. All you need is a good connection to the internet to access the course material that will be in the form of articles, videos, and knowledge checks.You are not required to attend any classes in person.