TOP 10 MACHINE LEARNING COURSES IN TORONTO, CANADA

Mastering Machine Learning: From Data Import to Model Evaluation with Advanced Classifiers

In the first part of this machine learning course, students get started in machine learning by implementing powerful supervised learning algorithms in Python using its allied packages, providing limited theoretical concepts and practical awareness of important learning algorithms.

The machine learning course is the perfect one for data analysts, junior data scientist, and python engineers who want to develop ML project experience, learn various industry use cases, and become a data scientist.

Fortunately, today’s data science methods are more practical and accessible than ever. The open-source R environment provides a straightforward yet incredibly powerful toolbox for performing useful predictive modeling and deep analysis.

This course gives you the opportunity to develop a broad understanding of machine learning, with no technical coding or programming knowledge required.

Envision Learning provides data analyst courses in the field of data analysis, focusing on techniques and tools used to extract insights and make informed decisions based on data.

This course covers the fundamentals of machine learning techniques ranging from various Support Vector Machines algorithms, k-means clustering, Random Forests, Collaborative filtering to recommendation systems, Mahout on Hadoop and Amazon EMR, etc.

The course starts with an overview of Azure services that support data science. From there, it focuses on using Azure's premier data science service, the Azure Machine Learning service, to automate the data science pipeline.

NetSoft’s Cloud Engineer Program trains students in Machine Learning. It enables students to gain the proficiency needed to harness the power of these products and other cloud and DevOps tools.

In this course, you will dive into the basics of machine learning using Python, a well-known programming language. Get introduced to data exploration and discover the various machine learning approaches like supervised and unsupervised learning, regression, classifications, and more.