Learning Path includes: – Machine Learning Full Course – Machine Learning Crash Course -2 Hours – What is Machine Learning? – Top 10 Books for Machine Learning – Top 10 Applications of Machine Learning – Machine Learning Tutorial – AI vs Machine Learning vs Deep Learning – How To Become A Machine Learning Engineer? – 10 Must Have Machine Learning Engineer Skills That Will Get You Hired – Machine Learning Engineer Jobs, Resume & Salary – Principal Component Analysis in Python – Python Tutorial for Beginners – Python Machine Learning Tutorial – How Netflix uses Python – Python Seaborn Tutorial – Scikit Learn Tutorial – Top 10 Python Libraries – Top 10 Reasons to Learn Data Science – K Means Clustering Algorithm – Logistic Regression in Python – Linear Regression Algorithm – Linear Regression vs Logistic Regression – Time Series Analysis in Python – Decision Tree Algorithm – Python Projects For Beginners – Python Certification – Machine Learning Interview Questions and Answers – KNN Algorithm using Python – Naive Bayes Classifier in Python – A Quick Guide To Sentiment Analysis – Reinforcement Learning Tutorial – Python Programming Language: Interesting Facts You Need To Know – Twitter Sentiment Analysis – Machine Learning With Python – Python Tutorial For Beginners – PyTorch vs TensorFlow: The Force Is Strong With Which One? – Azure Machine Learning Tutorial – Machine Learning Project Ideas For Beginners – Stemming And Lemmatization Tutorial – Natural Language Processing (NLP) – Supervised vs Unsupervised vs Reinforcement Learning – Data Science vs Machine Learning – What’s The Difference? – Probabilistic Graphical Models (PGMs) In Python – Apriori Algorithm Explained – Boosting Machine Learning – Python Tutorial For Beginners – Data Science for Beginners – Jupyter Notebook Tutorial – How To Become A Python Developer? – Artificial Intelligence For Beginners – What Is Machine Learning? – E & ICT Academy NIT Warangal Partners – What Is Artificial Intelligence? – Artificial Intelligence Tutorial for Beginners – Q Learning Explained – Data Science and Machine Learning for Non Programmers – Top Machine Learning Tools and Frameworks for Beginners – Best Python Libraries For Data Science & Machine Learning – Classification in Machine Learning – Hill Climbing Algorithm – Find-S Algorithm in Machine Learning – Cross-Validation In Machine Learning – EM Algorithm In Machine Learning – Mathematics for Machine Learning [Full Course] – Bias-Variance In Machine Learning – Python for Data Science – What Are GANs? – Generative Adversarial Networks Explained – What’s New in Pandas 1.0.0 – Future of Artificial Intelligence and Machine Learning – COVID – 19 Outbreak Prediction using Machine Learning – PUBG Data Science Tutorial – Part 1 – PUBG Data Science Tutorial – Part 2 – Stock Prediction using Machine Learning and Python – How to Select the Correct Predictive Modelling Technique – Top 10 Machine Learning Trends
FAQ
Most frequent questions and answers
Co-operative education is a three-way partnership between the university, students and employers. Students apply their classroom knowledge in a series of four-month work experiences. You, the employer, enhance a student’s education, while reaping the unique benefits of CO-OP employees.
- Year-round access to well-motivated, qualified employees.
- Access to potential full-time staff in a controlled environment, reducing your costs and risks.
- Access to a cost-effective source of temporary employees for peak periods or special projects.
- A say in what students learn by working with the university.
- Promotion of your organization as one that believes in developing the potential of young people.
- Access to a great pool of French-speaking, English-speaking and bilingual students.
Most work terms run at least 15 weeks, or four months. They can be no shorter than 13 weeks. Some master’s students, as well as some science and engineering students, are available for 8 or 12 months’ work terms.
All jobs are reviewed by a CO-OP Program Coordinator, and only those providing students with work experience related to their professional development are approved. Administrative activities involved in a job should be less than 10% of the entire workload.
When you first contact SSC, you are assigned one of our Program Coordinators, depending on your discipline of interest. This person is your main contact in our office. As you move through the recruitment process, you also work with a representative from CO-OP Administrative Services, who assists with job posting and interview scheduling.
Internships offer usually one discipline-specific, supervised, structured paid or unpaid, and for academic credit work experience or practice placement.
Work Experience intersperses one or two work terms (typically full-time) into an academic program, where work terms provide experience in a workplace setting related to the student’s field of study and/or career goals.
Community Service Learning (CSL) integrates meaningful community service with classroom instruction and critical reflection to enrich the learning experience and strengthen communities. In practice, students work in partnership with a community-based organization to apply their disciplinary knowledge to a challenge identified by the community.
Field Placement provides students with an intensive part-time/short term intensive hands-on practical experience in a setting relevant to their subject of study. Field placements may not require supervision of a registered or licensed professional and the completed work experience hours are not required for professional certification. Field placements account for work-integrated educational experiences not encompassed by other forms, such as co-op, clinic, practicum, and internship.
