Machine Learning

Learn to build models for prediction, use classification methods for building prediction models, and apply learning algorithms to improve predictive performance, using machine learning concepts.

Machine Learning Diploma Course in mississauga, brampton, toronto

Our MACHINE LEARNING program provides students with a broad knowledge in two main areas of Machine Learning: supervised and unsupervised. The program introduces to systems that learn from experience and outline the problems based on classification, clustering and regression.

It will cover topics Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines, Ensemble Learning with Bagging and Boosting, Random Forest , k-NN, Dimensionality ReductionPrincipal Component Analysis, K-means algorithm, Self-Organizing Feature Maps, Apriori algorithm, FP-growth algorithm, Dimensionality Reduction using Principal Component Analysis, Anomaly Detection and Semisupervised Learning.

The program will primarily use the Python programming language and assumes familiarity with linear algebra, probability theory, and programming in Python.

KNOWLEDGE & SKILLS GAINED

Machine Learning students will understand the foundations of Linear Regression, Linear and Logistic regression, Support Vector Machines and other models in machine learning. The course will expose to the fundamentals concepts of regression and classification with practical implementation with different Python packages like Numpy, Scipy and Scikit-Learn.

Student will be able to build models for prediction using machine learning concepts, use classification methods for building prediction models, apply learning algorithms to improve predictive performance, outline the required instances using representation learning, build an anomaly detection system by separating outliers, apply clustering algorithms for observations based on similarity and construct predictor’s models using machine learning techniques.

The project work will build your technical skills by providing a methodological approach towards problem-solving using models in Machine Learning.

NEXT PROGRAM START DATES

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January 11, 2021 May 3, 2021 MON, TUE, WED, THU, FRI 9:00 AM - 2:00 PM Online Campus Register Now
February 8, 2021 May 31, 2021 MON, TUE, WED, THU, FRI 9:00 AM - 2:00 PM Online Campus Register Now
February 26, 2021 June 22, 2021 MON, TUE, WED, THU, FRI 9:00 AM - 2:00 PM Mississauga - Malton Campus Register Now
July 19, 2021 November 8, 2021 MON, TUE, WED, THU, FRI 9:00 AM - 2:00 PM Mississauga - Malton Campus Register Now
November 15, 2021 March 7, 2022 MON, TUE, WED, THU, FRI 9:00 AM - 2:00 PM Mississauga - Malton Campus Register Now
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PRE-REQUISITES: Computer Knowledge

PROGRAM OUTLINE

Professional Skills - Trains on interpersonal skills required for successful induction and working in professional world.

Project+ -   This CompTIA training course helps you to prepare for CompTIA Project+ exam.

Introduction to Programming -   This instructor-led course is intended for students who want to learn how to write Python code that logically solves a given problem.

Database Fundamentals -   This MTA Training course helps you prepare for Microsoft Technology Associate Exam 98-364, and build an understanding of these topics: Core Database Concepts, Creating Database Objects, Manipulating Data, Data Storage, and Administering a Database.

Statistical Inference - The course introduces the fundamental concepts for understanding statistical analysis and its use in testing hypothesis and exploratory data analysis.

Mathematics for AI and ML - The course mathematical background for Artificial Intelligence and Machine Learning and covers  Linear Algebra, Analytical Geometry, Matrix Decomposition, Probability Theory, Vector Calculus, and Optimization.

Data Processing and Manipulation - This course will introduce the concepts of data processing, wrangling, manipulation, transformation, and operations on data using Pandas Library.

Data Visualization - This course will provide the necessary skills to interpret and present data clearly and visually appealing using Python libraries.

Supervised Machine Learning - This introductory course in machine learning covers the key concepts of supervised machine learning and different frameworks in practice for building machine learning systems.

Unsupervised Machine Learning - This course in machine learning covers the fundamental concepts for unsupervised machine learning techniques and includes topics such as the K-means algorithm, Self-Organizing Feature Maps, Apriori algorithm, FP-growth algorithm, and Dimensionality Reduction.

Capstone Project for ML -   This capstone project provides a hands-on learning experience for students to work independently on a specialized topic from Machine Learning.

WHY THIS PROGRAM?

Earning a Diploma in Machine Learning opens doors to a vast array of opportunities in IT industry. This diploma is a merging of the courses from the Machine Learning and Data Science and Big Data Analytics. Students are therefore trained in emerging applications areas of Machine Learning. Top IT employers are looking for software engineers who understand the complexity of data problems in industry and can collaborate towards problem solving using models of Artificial Intelligence and Machine Learning.

LEARNING STEPS TOWARDS OTHER SIMILAR PROGRAMS

Steps to Obtain PG Diploma in Artificial Intelligence and Machine Learning

ASSOCIATED NATIONAL OCCUPATION CLASSIFICATION (NOC) CODES

After earning a Diploma in Machine Learning, graduates are eligible for positions falling under the following NOC Codes

2173-Software engineers and designer’s research, design, evaluate, integrate and maintain software applications, technical environments, operating systems, embedded software, information warehouses and telecommunications software. They are employed in information technology consulting firms, information technology research and development firms, and information technology units throughout the private and public sectors, or they may be self-employed.

2171- Information systems analysts and consultants analyze and test systems requirements, develop and implement information systems development plans, policies and procedures, and provide advice on a wide range of information systems issues. They are employed in information technology consulting firms and in information technology units throughout the public and private sectors, or they may be self-employed.

2174- Computer programmers write, modify, integrate and test computer code for software applications, data processing applications, operating systems-level software and communications software. Interactive media developers write, modify, integrate and test computer code for Internet and mobile applications, computer-based training software, computer games, film, video and other interactive media. They are employed in computer software development firms, information technology consulting firms, and in information technology units throughout the private and public sectors.

JOB FUNCTIONS

Software Engineers and Information Technology Associates perform some or all of the following duties:
Software engineers and designers research, design, evaluate, integrate and maintain software applications, technical environments, operating systems, embedded software, information warehouses and telecommunications software. They are employed in information technology consulting firms, information technology research and development firms, and information technology units throughout the private and public sectors, or they may be self-employed.

  • Collect and document users' requirements and develop logical and physical specifications
  • Research, evaluate and synthesize technical information to design, develop and test AI and ML based systems
  • Build, test, and deploy AI models, as well as maintain the underlying AI infrastructure.
  • Develop data, process and network models to optimize architecture and to evaluate the performance and reliability of designs
  • Can navigate between traditional software development and machine learning implementations.
  • Plan, design and co-ordinate the development, installation, integration and operation of AI based systems.
  • Assess, test, troubleshoot, document, upgrade and develop maintenance procedures for operating systems, communications environments and applications software using AI development and production infrastructure.
  • Apply mathematical techniques to the solution of problems in scientific fields such as physical science, engineering, computer science or other fields such as operations research, business or management.
  • Collect, consolidate, cross-tabulate and format data from various sources to prepare draft reports for review by supervisors or researchers.
  • Process data using statistical software to conduct basic analyses of trends in support of research activities
  • Describe, prepare, format and scale the data infrastructure for analysis and implementation.
  • Train machine-learning based software for domain-specific tasks (Image recognition, object detection, bioinformatics, autonomous vehicles, etc)

May lead and co-ordinate teams of information systems professionals in the development of software and integrated information systems, process control software and other embedded software control systems.

JOB REQUIREMENTS

A bachelor's degree or college diploma in Machine Learning.

Job openings in the field of Information Technology usually seek a person with a diploma in Machine Learning or have studied related fields of study. Majority of the IT companies are looking for software engineers who can work with large datasets and can do transformations, visualizations and apply machine intelligence algorithms to solve complex business problems in the Industrial sector.

Job openings in the field of Artificial Intelligence and machine learning usually seek a person who can work with Python ML Libraries such as numpy, pandas, Scipy and Scikit-Learn. The person can contribute towards the development and deployment of Machine Learning algorithms to create operational models to develop complete, scalable systems.

EMPLOYMENT AREAS & PROSPECTS

Completing the Diploma in Machine Learning exposes students to various fields for hire, predominantly for Machine Learning Engineer, software engineers, information systems, information analysts, research analyst, data management, and data analysis in various IT industries. The technology sector in Canada is growing at an expansive rate which is allowing for new opportunities to arise in this field.

The job market for graduates with understanding in artificial intelligence, machine learning and deep learning in IT industry is projected to grow 56% in the next few years and create new jobs as the number of companies expanding their IT departments, are growing.

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Duration 16 Weeks - Full Time
Campus Mississauga - Malton
Course Fees Contact Us
Faculty IT
Course Level Intermediate
Features In Class, Assignment
Language English

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