Additional information
| Province | Ontario |
|---|---|
| Colleges | CIMT College |
| Program Categories | IT |
| Program Duration | 50 Weeks |
| Program Intakes | |
| Class Type | Hybrid |
| Funding | CSAP Funding, Second Career |
PG Diploma in Artificial Intelligence and Machine Learning
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Our POST GRADUATE DIPLOMA IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING is an extensive one-year diploma combines different disciplines of Artificial Intelligence and their application domains. The program provides an introduction to Artificial Intelligence and its broad discipline of intelligent agents and there use in building intelligent machines, Machine Learning introduces to systems that learn from experience and outline the problems based on classification, clustering and regression, and Deep Learning provides indepth knowledge for building Neural Networks and training models. Data Science and Big Data outline the way data can be made ready for analyses and can be used by learning algorithms.
KNOWLEDGE & SKILLS GAINED
Artificial Intelligence and Machine Learning students will understand the foundations of agents, and their working environment, along with various search strategies. They will also learn the key concepts of Machine Learning strategies such as supervised, unsupervised and reinforcement learning with practical implementation with different Python packages. The program introduces concepts in statistical inference, data manipulation, data visualization, neural networks, deep learning, big data and data analytics with IoT.
Students will get hands-on experience in various algorithms in Artificial Intelligence and Machine Learning algorithms using Python. Student will be able to apply deep learning techniques to virtual environments and will develop methods to generate synthetic data for training neural networks to achieve results comparable to networks trained on real data. Also they will be able to identify, analyze, and interpret trends or patterns in complex data sets and will gain experience with major big data technologies and frameworks including Hadoop, MapReduce, Apache Spark, and Hive. The project work will build your technical skills by providing a holistic approach towards problem solving using models in Artificial Intelligence, Machine Learning and Deep Learning.
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May 24, 2022
May 25, 2023
MON, TUE, WED, THU, FRI
9:30 AM – 2:30 PM
Mississauga – Malton Campus
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May 24, 2022
May 25, 2023
MON, TUE, WED, THU, FRI
9:30 AM – 2:30 PM
Online Campus
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September 12, 2022
September 21, 2023
MON, TUE, WED, THU, FRI
9:30 AM – 2:30 PM
Mississauga – Malton Campus
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September 12, 2022
September 13, 2023
MON, TUE, WED, THU, FRI
9:30 AM – 2:30 PM
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November 14, 2022
November 25, 2023
MON, TUE, WED, THU, FRI
5:00 PM – 10:00 PM
Mississauga – Malton Campus
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January 16, 2023
January 17, 2024
MON, TUE, WED, THU, FRI
9:30 AM – 2:30 PM
Mississauga – Malton Campus
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May 2, 2023
May 2, 2024
MON, TUE, WED, THU, FRI
4:00 PM – 9:00 PM
Mississauga – Malton Campus
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September 11, 2023
September 11, 2024
MON, TUE, WED, THU, FRI
4:00 PM – 9:00 PM
Mississauga – Malton Campus
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PRE-REQUISITES: Any college diploma OR university degree regardless of field. Any foreign credential must be assessed using WES or ICAS to determine its equivalency to a Canadian college degree or university diploma.
PROGRAM OUTLINE
Student Success Strategy?- Trains on techniques and skills to achieve success in personal and professional life.
Microsoft Word, Excel, and PowerPoint?- Introduces word processing, spread sheets, PowerPoint presentations.
English I?- Develops college-level grammar, vocabulary, sentence variety, paragraph structure, reading, and writing skills, which are necessary for success in all other courses.
Business Values and Ethics?- Introduces the basic categories and framework of business ethics.
Business Communication?- Introduces the principles of effective written and oral communication.
Professional Skills -?Trains on interpersonal skills required for successful induction and working in professional world.
Organizational Change Management – Looks at logical reasons why organizations must change, and why people and companies are resistant to change.
Project+? – This CompTIA training course helps you to prepare for CompTIA Project+ exam.
Cross Culture Management? – Examines the challenges and opportunities of managing cultural diversity in organizations with emphasis on the transnational enterprise.
Leadership – Looks at dynamics of leadership, initiation and management in the face of changing environment serving the organization.
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.
Software Development Fundamentals? – This MTA Training course helps you prepare for Microsoft Technology Associate Exam 98-361, and build an understanding of these topics: Core Programming, Object-Oriented Programming, General Software Development, Web Applications, Desktop Applications, and Databases.
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.
Principles of Artificial Intelligence – The course covers fundamental concepts of AI, the structure of agents, and their working environment, problem-solving through search, search strategies, reasoning with propositional logic, inference using predicate logic, and knowledge representation.
Probabilistic Reasoning and Decision Making? – This instructor-led course introduces the core concepts of Artificial Intelligence with a focus on probabilistic modeling and decision making under uncertainty.
Capstone Project for AI? – This capstone project provides a hands-on learning experience for students to work independently on a specialized topic from artificial Intelligence.
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.
Neural Networks and Deep Learning?-? This course presents the basic fundamentals for building Neural Networks and Deep Learning training models and covers topics like Multi-Layer Feed Forward Networks, Restricted Boltzmann Machines, Autoencoders, Convolutional Neural Networks, Recurrent Neural Networks, and Recursive Neural Networks.
Deep Learning in Practice? – The course familiarizes with essential Deep Learning architectural implementations in various applications such as Computer Vision, Recommender Systems, Text Analysis and Sequencing, and Natural Language Processing using TensorFlow.
Capstone Project for Deep Learning? – This capstone project provides a hands-on learning experience for students to work independently on a specialized topic from Deep Learning.
Big Data Analytics with Hadoop? – The course will provide the ability to identify the characteristics of datasets and to select and implement Big Data models.
Big Data Tools and Techniques – This course will introduce students with Apache Spark, which improves Big Data Processing and will provide insights to developing and protyping applications, application execution on clusters and High-level libraries such as Spark SWL and MLib.
Data Analytics for IoT? – This course provides an understanding towards analytics on IoT data and covers topics such as ?IoT Devices and Networking protocols, IoT Analytics for Cloud, Strategies to collect and Explore IoT data, Visualization and Dashboards for IoT data, Geospatial Analytics to IoT Data, and organizing data for Analysis.
Capstone Project for Data Analysis and Big Data? – This capstone project provides a hands-on learning experience for students to work independently on a specialized topic from Data Analysis and Big Data.
WHY THIS PROGRAM?
Earning a Post Graduate Diploma in Artificial Intelligence and Machine Learning opens doors to a vast array of opportunities in IT industry. This diploma is a merging of the courses from the Artificial Intelligence, Machine Learning, Deep Learning, Data Science and Big Data Analytics. Students are therefore trained in emerging applications areas of Artificial Intelligence and 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
ASSOCIATED NATIONAL OCCUPATION CLASSIFICATION (NOC) CODES
After earning?a?Post Graduate Diploma in Artificial Intelligence and 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.
2172 – Data entry clerks input coded, statistical, financial and other information into computerized databases, spreadsheets or other templates using a keyboard, mouse, or optical scanner, speech recognition software or other data entry tools. They are employed in the private and public sectors.
2161 – Mathematicians and statisticians research mathematical or statistical theories, and develop and apply mathematical or statistical techniques for solving problems in such fields as science, engineering, business and social science. Actuaries apply mathematics, statistics, probability and risk theory to assess potential financial impacts of future events. Mathematicians, statisticians and actuaries are employed by universities, governments, bank and trust companies, insurance companies, pension benefit consulting firms, professional associations, and science and engineering consulting firms.
1254 – Statistical officers and related research support occupations in this unit group provide statistical and research support services to a wide range of businesses and organizations. These workers conduct statistical routines, monitor trends, compile data and prepare charts, graphs, summaries and reports in support of organizational information needs and research activities. They are employed throughout the private and public sectors. Statistical officers who are also supervisors are included in this unit group.
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 Artificial Intelligence, Machine Learning, Deep Learning, Big Data Analytics, and Data Science
Job openings in the field of Information Technology usually seek a person with diploma in Artificial Intelligence and Machine Learning or have studied related fields of study. Since majority of the IT companies are looking with software engineers with knowledge in building, designing and testing AI models.
Job openings in the field of Information Technology usually seek a person who has completed college or other Artificial and Machine Learning courses. The person is able to combine AI and machine learning expertise with business and industry problems and can design and develop scalable solutions. The person can participate in the analysis, architecture, design and engineering of Artificial Intelligence, Machine Learning and Big Data platform technologies and components and can provide input to software development processes.
While entry level jobs ask for no experience or one to two years experience, higher positions can be sought for once you gain some practical experience in the actual workplace.
EMPLOYMENT AREAS & PROSPECTS
Completing the Post Graduate Diploma in Artificial Intelligence and Machine Learning exposes students to various fields for hire, predominately for 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 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.
