Machine learning classification is the process of using past data points to teach a model to predict the class of a new unlabeled data point. For business and finance professionals, the applications are vast ranging from optimizing marketing campaign spends to predicting whether a certain asset class will increase or decrease in value.
In this course, participants will learn about the most popular classification models used in industry, including decision trees, support vector machines, and neural networks. This workshop builds on Introduction to Data Science and essentially dives deep into the classification section of the introductory course. Attendees will learn how to build machine learning classification models in Python and evaluate and interpret model performance. The training also covers deep learning, which is a more involved form of machine learning. The workshop concludes with an in-class project to give participants the chance to apply the concepts taught in the workshop to solve a realistic problem.
Attendees can use either a Mac or Windows computer for this course.
An overview of machine learning classification methods and how they can be applied to solve real-world business and finance problems
All of our classes use relevant, Python-based exercises to help students apply theory to practice. Students write their own Python code during the workshop
Understanding of how to take very complex problems and break them down into manageable parts
Understanding of effective data visualization techniques, how to execute data visualization tasks in Python, and how to communicate results to key stakeholders
Participants will have opportunities to discuss current machine learning topics with other attendees & their Cognitir instructor and brainstorm practical strategies to improve decision making
All Cognitir participants receive course notes, a certificate of completion, instructions on how to add the course to their LinkedIn profiles, and post-seminar email support
This course is relevant for business and finance professionals who want to become more data-driven decision makers and use models to classify new data points. Nonbusiness professionals in law, medicine, politics, public health, public policy, and sports analytics would also benefit from enrolling in this workshop.
Prior Python programming experience is required.
Not sure if this is the right course for you? Contact us.
Yes. You must have prior Python programming experience and a core data science foundation before taking this course. The most efficient way to obtain both is to enroll in our Introduction to Data Science course.How is this course different from Introduction to Data Science?
This course is focused purely on the creation and evaluation of classification models. Introduction to Data Science is a thirty-thousand foot view of data science and covers most of the main data science genres. One of those genres is classification, and this course is a deep dive into the topic.How is this course different from Introduction to Time Series?
This course is focused purely on the creation and evaluation of classification models. Time Series models are not covered in this course.Do I have the right laptop for this course?
You will need a PC (Windows Vista or newer), Mac (OS X 10.7 or newer), or Linux (Ubuntu, RedHat and others; CentOS 5+). Please contact us if you are planning to use a different setup for this course.Do I need to install software prior to the class?
Yes, you will need to download and install free Python software on your laptop. In case you are planning on using a company laptop, please make sure that you have the necessary rights to install the tools. We will send out detailed information on the download and installation procedures a few days before the course starts.
Visit our FAQs to see commonly asked questions from participants about data science.