Data Science

Applied Machine Learning

US Online

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
Talk to our Advisor
Part-time
Online Live
10 weeks
100
Upcoming Start Date
Aug 27
Registration Deadline:
  August 27, 2024
View more start dates

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

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 programs 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 SKILLS
  • 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 SKILLS
  • 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 SKILLS
  • 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 SKILLS
  • 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 SKILLS
  • 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 SKILLS
  • 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 SKILLS
  • 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.

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

Aug 27 -
 Sep 11
Online Live
$3,800.00 USD

Explore your personalized learning path

Applied Machine Learning
$3,800 USD
  • Case-based learning
  • Portfolio project mentoring
  • Flexible payment plan
Recommended Short Courses
$2,600 - $5,200 USD
  • 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 ⇩
Upgrade to Bootcamp
$6,000 - $12,000 USD
  • 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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
View our Applied Machine Learning course package
View our Applied Machine Learning course package