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Applied Machine Learning Course - Jodie Zhu (WeCloudData)
This document outlines the syllabus for an applied machine learning course, focused on key topics such as machine learning lifecycle, data wrangling, and model deployment. It is designed for students and professionals looking to enhance their machine learning skills and includes hands-on projects, mentorship, and learning support. Prerequisites include some familiarity with Python, numpy, and pandas, and the course aims to prepare participants for real-world analytics problems.
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Applied Machine Learning Course - Jodie Zhu (WeCloudData)
- 1. Data Science Applied MachineLearning Syllabus
- 2. Data Science Track LearningPath Prerequisites Data Science Learning Path Build your portfolio with hands-on ML projects Applied Machine Learning Master data wrangling with Python Data Science w/ Python • Harness big data with Hadoop, Hive, Presto, and AWS • Machine Learning at Scale with Spark ML and Real-time Deployment Big Data for Data Scientists
- 3. Lecture Content LectureContent 1 Introduction • Introduction to Machine Learning • Gradient Descent 7 Advanced Ensembles • Xgboost • Stacking 2 Regularization • Regularization • Lasso/Ridge/ElasticNet 8 Model Interpretation • Factorization Machines • Complex Model Interpretation 3 Logistic Regression • Logistic Regression • Multi-class Classification • Evaluation Metrics • Variance/Bias Tradeoff 9 Unsupervised • K-Means Clustering • Dimension Reduction • PCA 4 Feature Engineering • Numerical Features • Categorical Features • Text Features 10 Neural Networks I • Neural Networks • Backpropagation 5 Non-parametric Models • KNN • Decision Trees • Project kick-off 11 Recommendation Engines • Market Basket Analysis • Collaborative Filtering • Matrix Factorization 6 Parameter Tunings • Ensemble Methods • Bagging • Boosting • Hyper-parameter Tunings 12 Model Deployment • Machine Learning Lifecycle • Model Deployment • Project Presentation Syllabus Applied Machine Learning Syllabus (Saturday Cohort – 12 sessions/48 hours)
- 4. Data Scientist Jodie Zhu •Machine Learning Engineer @ Dessa • Lecturer @ University of Toronto • Python Instructor at WeCloudData • Expertise: Python | Data Science | Deep Learning Machine Learning Engineer Dessa & Adjunct Lecturer University of Toronto
- 5. Python Programming Instructor –Holly Xie • Machine Learning Scientist at integrate.ai • University of Waterloo, Master of Mathematics • Machine Learning Instructor at WeCloudData • Expertise: Machine Learning| Deep Learning Machine Learning Scientist Integrate.ai
- 6. Applied Machine Learning Whois this course for? • This course is designed for students and professionals who want to learn machine learning techniques and apply them to solve real-world analytics problems. • For new graduates and job seekers, this course teaches you the essential machine learning skills required for data scientist jobs • For SAS or R predictive modelers, the course will give you a smooth transition to the python machine learning ecosystem • For AI enthusiasts who later want to learn advanced machine learning techniques and applied deep learning, this course will help you build the critical foundations • For developers and engineers who want to gain machine learning knowledge to build ML-driven applications, this course teaches you ML theory and statistics at the right level to make your work productive • For tech-savvy product managers who want to gain a comprehensive understanding of machine learning use cases and lifecycles, the hands-on project in this course gives you exactly what you hope for.
- 7. Applied Machine Learning Prerequisites Prerequisites •Some familiarity with Numpy and Pandas. • Has taken the Python for Data Science course or equivalent • Understanding of statistics and linear algebra is not a must since it will be covered in the pre-course on-demand learning materials but prior experience definitely helps! • Having an understanding of your firm's data, technologies, and predictive modeling goals will motivate and direct your focus in this program
- 8. Applied Machine Learning Learningoutcome After this course, the students will be able to • Understand the core concepts of the machine learning project lifecycle • Understand the key machine learning steps including data exploration, feature engineering and selection, model training and evaluation, parameter tuning, and model interpretation • Use Python’s ML tools such as Scikit-learn to train various classification and regression models such as linear regression, decision trees, logistic regression, support vector machine, and etc. • Understand different Machine Learning use cases and most importantly how to apply best practices to real company data problems • Gain real world experience through a hands-on project and convince your manager/peers that you’re up for more advanced analytics projects at work
- 9. Applied Machine Learning Hands-onProject This course is instructor-led and project-based. Students will be able to apply the Machine Learning knowledge acquired in the course to a hands-on project. Project: • The instructor will work with the students to decide the project topics. It is highly recommended that the students bring their own motivation and ideas. Otherwise, a topic along with datasets will be assigned to the students • The student is also encouraged to apply the learnings directly to his/her company’s data problems and receive technical advice from the instructor
- 10. Applied Machine Learning LearningSupport Support you will receive during this course include • Mentorship and advice from an industry expert • In-classroom learning assistance by our assistant instructor • Online learning support on Slack from instructor and TA • Hands-on labs and projects to help you apply what you learn • Additional resources to help you gain advanced knowledge • A repository of common ML interview questions and quizzes • Help from our learning advisor on how to choose the learning path and specialization courses after this course
- 11. Applied Machine Learning Testimonials Agreat place to learn and practice data science. I am taking Machine Learning course currently, and the instructor Vanessa 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. Minjung Koo Student Testimonial I was searching for a data science 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). Yin Zhao
- 12. Applied Machine Learning Howto convince your employer Do you know that most employers will reimburse the training costs? • We have a detailed course syllabus and email template that you can use to convince your manager that this is the right course for you and a good investment for your company • You will have a completed project and presentation that you can use to demo to your manager and showcase your newly minted machine learning skills and get ready for more interesting advanced analytics projects
- 13. Lecture Content LectureContent 1 Introduction • Introduction to Machine Learning • Gradient Descent 7 Advanced Ensembles • Xgboost • Stacking 2 Regularization • Regularization • Lasso/Ridge/ElasticNet 8 Model Interpretation • Factorization Machines • Complex Model Interpretation 3 Logistic Regression • Logistic Regression • Multi-class Classification • Evaluation Metrics • Variance/Bias Tradeoff 9 Unsupervised • K-Means Clustering • Dimension Reduction • PCA 4 Feature Engineering • Numerical Features • Categorical Features • Text Features 10 Neural Networks I • Neural Networks • Backpropagation 5 Non-parametric Models • KNN • Decision Trees • Project kick-off 11 Recommendation Engines • Market Basket Analysis • Collaborative Filtering • Matrix Factorization 6 Parameter Tunings • Ensemble Methods • Bagging • Boosting • Hyper-parameter Tunings 12 Model Deployment • Machine Learning Lifecycle • Model Deployment • Project Presentation Syllabus Applied Machine Learning Syllabus (Saturday Cohort – 12 sessions/48 hours)
- 14. TYPE OF DATAJOB SEEKERS 传感器 数据 机器学 习 人工智 能 机器人 行动 触发器