Machine Learning Course – Toronto Campus
Machine Learning Course
This Machine Learning Course consists of two parts: Fundamentals of Machine Learning and Advanced Machine Learning. 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 by providing a limited theoretical concepts and practical awareness of important learning algorithms. Students will get hands-on experience with the rich functionality provided by Python and other packages for implementing linear and logistic regressions, decision and regression tress, Naïve Bayes classifier, dimensionality reduction, support vector machines, and model evaluation and cross validation.
In the second part of the course students will develop their advanced modeling techniques through unsupervised learning techniques (e.g. clustering), time-series forecasting, anomaly detection, training with artificial neural networks and deep belief networks and feature engineering. Particularly, students will learn clustering to detect patterns and structure within data sets, know how to transform data to make machine learning feasible, know how to enhance data, etc. The learning outcome of this course is to make students understand data sets from diverse domains and perform all sorts of data analysis such as descriptive, exploratory, predictive, and inferential using Python.
- Data Mining
- Theoretical knowledge and understanding of the three types of machine learning: supervised, unsupervised, and reinforcement
- Basic knowledge of uni-variate calculus and basic linear algebra concepts
TOPICS OF THE MACHINE LEARNING COURSE
- Introduction to Machine Learning and its role in Data Science
- Math – Overview of Linear Algebra, Euclidean Distance, KNN and K-means
- Explore Classification through KNN and Clustering through K-means
- Linear Classifier – Perceptron, Optimization and Understanding Gradient descent
- Logistic Regression, Regularization, Feature Normalization Bias and Variance
- Multi-class classification, SVM, Kernel Methods
- Non-linear classification, Introduction to Decision Trees and Random Forest, Neural Networks and Ensemble Learning
- Natural Language Processing (1)
- Natural Language Processing (2)
WHO SHOULD TAKE THIS COURSE?
Data analysts, financial analyst, marketing analyst, reporting analysts, data scientists, statisticians, computer programmers, students of data analytics, …
WHO IS THE LEAD INSTRUCTOR OF THIS COURSE?
Ms Gitimoni Saikia is also the instructor of this course. She has a master in computer science and 7+ years of successful work experience in the education field. She has taught many programming languages, data structures and machine learning and has gained in-depth knowledge of machine learning algorithms including deep-learning and applied them to many projects using tools in Python, C++ and Java. Her areas of research interests include computer vision, natural language processing and financial analytics. Currently, Ms. Saikia is working as a data science consultant to many corporations in Toronto and also teaching data science courses at Metro College of Technology, Toronto, Ontario, Canada.
Credential: College certificate.
Location of course delivery: Toronto, Ontario,
Please call 416 585 9880 for schedule and fee information
If you are interested in a career as data analyst or data scientist, please visit our Data Science and Application program.