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. It teaches core ML algorithms and methodologies in a practical way. Students will be ready for data scientist and junior machine learning engineer roles after completing this course.

Course highlights

Hands-on practice
Case-based learning
ML portfolio building

Intermediate

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Part-time

Online Live

10 weeks

100

Upcoming Start Date

Feb 27

Registration Deadline:

February 27, 2024

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About the Course

This course can be taken as a part of WeCloudData’s Data Science Bootcamp. It teaches you the practical knowledge and problem solving skills you need to excel in a data scientist role. It focuses on case-based learning and project building.

What you will learn
- Mathematics for ML
- Classical Machine Learning algorithms
- Feature Engineering and Selection
- ML Pipelines
- 7+ ML industry use cases
- How to build end-to-end ML projects

Case-based learning with real-life datasets
- Sales Prediction
- Customer Analytics (Acquisition/Churn/Segmentation)
- Audience Expansion & Look-alike Models
- Supply Chain – Demand Forecasting
- Competitive Intelligence
- Sentiment Analysis
- Credit Risk Modeling
- Fraud Analytics

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WeCloudData is the perfect place to grow your career

 5/5

Minjung Koo

A great place to learn and practice data science. I am taking the Machine Learning course currently, and the instructor is amazing, and I get a lot of hands-on exercises, and feedback. I like that the course is not only teaching you how to code, but also teach you the fundamental theories of each tool, and how to apply in the real-life business problems. I highly recommend all their courses to anyone who wants to become a data scientist.

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CURRICULUM

Be ready for the new economy

WeCloudData Bootcamps are designed to be project-based. We not only cover essential theories, but also teach how to apply tools and platforms that are in high demand today. Our program curriculum is also highly adaptive to the latest market trends.

Module 1

Mathematics for Machine Learning

You don’t need a Math degree to build and train machine learning models. However, knowing enough math fundamentals will make your learning journey much more effective and enjoyable. This module of the course teaches you essential math concepts that you will need to know how to fine-tune and improve ML model performance.

LEARNING OUTCOMES

Understand the statistics concepts that form the building blocks of machine learning
Learn enough linear algebra to manipulate vectors and matrices and vector/matrix operations
Understand the basics of derivatives and integrals that will assist your understanding of advanced ML algorithms
Understand the mechanics of gradient descent and basic optimization techniques

Key Skill:

Statistics, Linear Algebra, Calculus, Optimization, Distribution, Significance Testing, Matrix Operations, Vector Operations, Gradient Descent

Tools:

Pandas

Numpy

Scipy

Matplotlib

Tools:

Sklearn

Pandas

Module 2

Supervised Learning

In this module you will learn to build your first machine learning model. You will learn some of the most common yet powerful ML algorithms in the supervised learning category. This is the moment when your ML journey starts to become enjoyable and you'll be able to start tackling real-world ML problems and build mini-projects.

LEARNING OUTCOMES

Linear Regression
Regularization
Decision Boundaries
Logistic Regression
Decision Trees
Evaluation Metrics
Model Validation

Key Skill:

Linear Regression, Logistic Regression, Model Evaluation, Model Validation, Cross Validation, Model Accuracy, AUC-ROC, Precision & Recall, Regularization, Decision Trees

Module 3

Feature Preparation

This module covers one of the most important aspects of machine learning. Feature preparation, if done right, can become a powerful weapon. Experienced data scientists usually spend more effort on feature engineering and feature selection, which are processes of removing noise from the ML dataset to boost the predictive power of the models. We will introduce many useful and practical ML feature engineering tips and tricks in this module to help boost the experience level of learners.

LEARNING OUTCOMES

Handle imbalanced dataset via resampling strategies such as under- and over-sampling
Understand the pros and cons of common feature engineering techniques in different business contexts
Learn how to engineer features for structured and unstructured data
Learn how to select useful features to boost model performance or explainability

Key Skill:

Resampling, Feature Engineering, Feature Selection, SMOTE

Tools:

Python

Sklearn

Tools:

Python

Sklearn

Hyperopt

Module 4

Ensemble Models

This module covers advanced ML algorithms such as bagging and boosting. You will learn how to further improve ML model performance by applying ensemble learning and hyper-parameter tunings.

LEARNING OUTCOMES

Ensemble Learning
Ensemble Algorithms
- Random Forest
- Gradient Boosting
- XGboost
- Catboost
Hyperparameter Tuning

Key Skill:

Ensemble Learning , Random Forest, Gradient Boosting, XGboost , Catboost, Lightgbm, Hyperparameter Tuning

Module 5

ML Pipelines

This module covers the practical considerations for applied machine learning. We will teach learners how to leverage ML Pipeline features to build reproducible pipelines that help reduce errors in production. Students will also learn how to interpret machine learning output to the business, from technical techniques to more business friendly ones.

LEARNING OUTCOMES

Learn how to write elegant and reproducible ML code using ML Pipelines
Interpret machine learning models using popular techniques such as LIME and SHAP
Learn how to profile ML predictions and help the business understand key predictors in layman’s terms

Key Skill:

Model Interpretation, LIME, SHAP, ML Pipeline, Model Profiling

Tools:

Sklearn

SHAP

Tools:

Sklearn

Numpy

Module 6

Unsupervised Learning

This module introduces learners to the unsupervised learning world. Learners will learn techniques such as Clustering and Isolation Forest that are commonly used for anomaly detection. Business use cases such as clustering and dimension reduction are also introduced.

LEARNING OUTCOMES

Apply clustering algorithms for customer segmentation
Reduce and visualize high dimensional data using PCA and t-SNE
Detect anomalies in data using Isolation Forest and Clustering algorithms

Key Skill:

Dimension Reduction, K-Means Clustering, Hierarchical Clustering, Unsupervised Learning, Principal Component Analysis, t-SNE, Customer Segmentation, Anomaly Detection

Module 7

Introduction to Deep Learning & AI

This module introduces learners to the world of AI. Students will learn how to apply deep neural networks to solve complex non-linear problems. A brief introduction to computer vision and NLP will be covered so students have basic skills required for processing and analyzing text and images.

LEARNING OUTCOMES

Understand Neural Networks
Use Pytorch to train image classification models
Learn to pre-process text data
Discover topics in text data using Gensim and LDA
Understand how to prepare text features using TF-IDF and vecotorization

Key Skill:

Neural Networks, PyTorch, spaCy, NLTK, Text Pre-processing, Topic Modeling, Deep Learning, CNN, Image Classification, Text Classification

Tools:

Sklearn

PyTorch

spaCy

FACULTY TEAM

Learn from the best

We’ve brought together a team of highly skilled and experienced instructors to help you learn effectively. Our instructors have a passion for teaching and a wealth of real-world experiences in their respective fields, so you can be confident that you’re learning from the best.

Shaohua Zhang

Chief Instructor (WeCloudData) | Head of Data Science (BeamData) | Startup Advisor and Data Coach

Shaohua is the Co-founder and Chief Instructor at WeCloudData. In the past few years, he has trained hundreds of students and helped many of them launch their data science careers. He is also the CEO and Head of Data Science at Beam Data where he works closely with the industry partners on implementing data science projects. Prior to co-founding the Toronto Institute of Data Science and Technology (WeCloudData), he led the data science team at BlackBerry and helped build the data science practice at Kik. He has also worked with big companies such as TD, Canadian Tire, Sunlife, CIBC, Communitech, and MaRS on upskilling their data teams.

Jodie Zhu

Machine Learning Engineer | Instructor | Adjunct Professor

Jodie is a Data Scientist with industry experience in areas including AI startup, mobile games, public health, and within the pharmaceuticals industry. Currently, she is a Machine Learning Engineer at Dessa. Previously, she worked as a Data Scientist at Gameloft, where she works on an iOS top 10 mobile game, doing a combination mix of user behavior research, exploration, data engineering, and statistical modeling. She is a master of digital marketing campaign design and analysis, as well as implementing various statistical and machine learning models to help solve real-world business problems. She has also achieved the M.Sc. degree in Biostatistics from the University of Toronto. Before coming to Toronto, she was enrolled in the Applied Mathematics Ph.D. program at the University of Florida.

Ken Ding

Senior Manager, Credit Risk Modeling - Decision Science, BMO Financial Group | Data Science Instructor | Career Mentor

Ken has over 9 years of experience in data analytics and specializes in retail lending, campaign analytics and credit risk modeling. With a passion for Data Science, Ken has acquired solid technical skills (SQL/Python/SAS/AWS) and would like to share his learning experience with everyone. Ken also received an MBA degree from Rotman Business School, University of Toronto.

Rogelio Cuevas

Director, Data Scientist (Sun Life) | Data Science Instructor (WeCloudData) | Career Mentor (WeCareer)

Rogelio Cuevas currently works as a Director of Data Science in the financial industry in Toronto and as a Data Science mentor for a San Francisco start-up. His educational background has allowed him to work in various research-oriented institutions that include Duke University, McMaster University, University of Western Ontario, and Fermilab. His past research activities made him a recipient of a postdoctoral Fellowship awarded by the Natural Science and Engineering Research Council of Canada.

Project

Portfolio Experience Building

Making yourself hireable and stand out from the crowd by working on personal projects. Here’s what you will experience:

Pick an industry to focus on
Research ML use cases for that industry
Find or collect datasets for the project
Write a project proposal
Build your ML portfolio project
Code review with your learning mentor
Present your portfolio project
Publish your work online

SCHEDULE

Upcoming Start Dates

Nov 21 -

Feb 10

Online Live

$5,000.00

Feb 27 -

May 02

Online Live

$5,000.00

Explore your personalized learning path

Applied Machine Learning

$5,000

Case-based learning
Portfolio project mentoring
Flexible payment plan

Recommended Short Courses

$3,500 - $7,000

Learn advanced skills to stand out
Get alumni discount for the AI, MLOps, and Big Data courses
Short courses to consider after completing this course ⇩

AI Course – NLP/LLM

Learn More >

Big Data for Data Scientists

Learn More >

Upgrade to Bootcamp

$8,000 - $16,000

Upgrade to the ML Engineering bootcamp and enjoy alumni discount
Get extensive 1-1 career mentoring and job support
Get the flexibility to create your own bootcamp
Work on real client projects to build experience

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What our graduates are saying

 5/5

Yin Zhao

I was searching for a machine learning courses in GTA and WeCloudData grabbed my attention for 1. how professional their staffs are, 2. the industry experience the lecturers come with, and, 3. most importantly, that they regularly review and update their course material in response to the trends in the data science job market.
I ended up taking several courses from them. They’re much like a real school set-up that I actually felt the peer pressure to keep up with the take-home assignments and projects (which is def a good thing for part-time students).

 5/5

Jason Lee

Thank you so much for coordinating an awesome course. The assistant instructor was really great in exposing to us how course material is applied in production level environment. I also like the instructor’s approach of pushing us to build from fundamental to real project using PyTorch first. And then progressing to Tensorflow. Personally, I think it’s a super awesome course, but I believe you really have to dig yourself into the contents and dedicate many head banging hours. But course material is very practical both in theory and application. Thanks much!

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FAQ

Frequently asked questions about the bootcamp

Are there pre-requisites for this course?

Learners joining this course will need to have solid python programming and data processing skills. Experience with numpy and scipy will be helpful too. We strongly recommend learners take the Data Science course first before taking this course. You can take them as a bundled package as well.

How hands-on is the machine learning course?

The Applied Machine Learning course is very hands-on by design. Learners will start to work on a capstone project starting from the 2nd week. There’re lots of exercises that will keep learners busy. The lectures are also taught in a hands-on fashion. Learners will follow instructors and TAs to complete labs.

Will I be able to start job searching after taking this course?

Yes, you will have the right skills to apply for data science jobs. When the job market is very competitive, you will definitely need to learn more in order to stay competitive. But this course will teach you the right ML skills to get started. Other courses we would recommend include WeCloudData’s big data course and other AI, MLOps courses.

Are the skills taught in this course in demand?

Yes, 100%. With the recent boom in generative AI and tools like ChatGPT and Midjourney, the entire world has high expectations for AI and automation. AI is going to make every data scientist become more efficient and achieve more in their jobs. Machine learning is a very important aspect of AI and anyone who wants to become specialized in AI technologies will need to learn machine learning. This course covers the necessary math skills, feature engineering techniques, machine learning workflows, and popular machine learning algorithms. All of these skills and tools are in good demand in the job market.

What will the learner build in the capstone project?

Learns will build an end-to-end machine learning project to be added to their data science portfolio. You will be required to come up with your original ideas, choose your own datasets, carry out industry use case research, and then build a machine learning pipeline to solve a practical ML challenge. At the end of the project, you will be presenting your work to experienced instructors and the entire class to get feedback.

How much business context does this course teaching?

This is the key differentiator of WeCloudData’s machine learning course. We not only teach students tools, but also introduce lots of industry use cases. In fact, we also use some real-life industry datasets for teaching and students will learn how to APPLY data science skills in various business contexts.

How much learning support do I get in the course?

You can communicate with the instructors and teaching assistants (TA’s) regularly through our online platform and communication app (Slack). We host live lab sessions and office hours. On-demand 1-1 support is also available.

What if I miss a few sessions?

The live lectures are all recorded so you will be able to watch them and catch up in case you miss a class. You can also join the office hours to ask questions about the missed lessons.

Do you offer financial support?

Yes. We offer scholarships, flexible payment plans, and student loans via 3rd party lenders. Please fill out the form on the course page to get in touch with our learning advisors.

Will my employer cover the expense for this type of course?

Some students have their employers cover the tuition. You can always ask your employers about it. We’re happy to provide the curriculum and enrolment letter.

WeCloudData is the leading data science and AI academy. Our blended learning courses have helped thousands of learners and many enterprises make successful leaps in their data journeys.

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About the Course

WeCloudData is the perfect place to grow your career

Ranked #1 Data Training Program

CURRICULUM

Be ready for the new economy

Key Skill:

Tools:

Tools:

Key Skill:

Key Skill:

Tools:

Tools:

Key Skill:

Key Skill:

Tools:

Tools:

Key Skill:

Key Skill:

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FACULTY TEAM

Learn from the best

Project

Portfolio Experience Building

SCHEDULE

Upcoming Start Dates

Explore your personalized learning path

Applied Machine Learning

Recommended Short Courses

AI Course – NLP/LLM

Big Data for Data Scientists

Upgrade to Bootcamp

Data Engineering Bootcamp

Machine Learning Engineering Bootcamp

Data Science Bootcamp

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student success

What our graduates are saying

Let WeCloud Accelerate Your Career in Tech

Have questions?

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View our Applied Machine Learning course package