Machine Learning for Developers with Scikit-Learn (Cohort 4)

Feb 13, 10:00AM PST(18:00 GMT) | Sat
Description
Syllabus
Instructors
Student Reviews
COURSE OBJECTIVES:
In this course you will learn the fundamentals of machine learning including intuitions, important theoretical aspects and how to use machine learning algorithms to solve problems. Students will learn about the foundational underpinnings of machine learning as well as how to put that knowledge to the test with practical exercises.

The course takes projects focused approach to teach you machine learning by building machine learning models and projects. The instructor will walk you through a series of curated projects, and explain the key concepts as they arise. Students will learn the theory and how these models work under the hood while writing code.

The course balances learning theory, coding exercises, and working on projects. Students who take this course will be able to:

  • Identify and frame problems that can be solved by machine learning
  • Choose the right techniques to the problems
  • Understand key machine learning concepts and how algorithms / models work
  • Build and training various models with scikit-learn
  • Troubleshoot and improve models
  • Discuss the parts and processes involved in building large scale machine learning applications
  • COURSE SCHEDULE:
    • Session 1: Feb 13th 10am~12pm PST (US Pacific Time, GMT-8)
    • Session 2: Feb 13th 12pm~2pm PST
    • Session 3: Feb 20th 10am~12pm PST
    • Session 4: Feb 20th 12pm~2pm PST
    • Session 5: Feb 27th 10am~12pm PST
    • Session 6: Feb 27th 12pm~2pm PST
    • Session 7: Mar 6th 10am~12pm PST
    • Session 8: Mar 6th 12pm~2pm PST

    COURSE INCLUDE:
    • 4 weeks / 8 sessions / 16 hours
    • 8 lectures / 4 hands-on code labs/Capstone project
    • Live Sessions (with zoom), Real time interaction
    • Slack support during and after class

    COURSE CONTENT:
    Check the Syllabus tab for full course content.

    WHO SHOULD LEARN:
    Developers, data scientists, students.

    PREREQUISITE:
  • Basic familiarity with python
  • Using Jupyter Notebook or Colab
  • Difficulty Level
    Beginner~Intermediate
    Est. time spend per week: 4 hours live class (required) + 2 hours homework (required) + 2 hours projects (bonus, optional).

    FREE TRIAL
    Full refund upon request before the first session ends (Feb 13th, 2021 12:00pm PST). 5% transaction fee is not refundable.

    SESSION REPLAY
    If missed live sessions, you can watch recordings any time, along with interactive learning tools, slides, course notes
    Students have one-year access to all course materials

    BENEFITS
  • Earn Certificate of Completion
  • Financial aid is available for application, contact for details
  • PREVIOUS COHORTS:
  • Cohort 3: Nov 23 ~ Dec 16, 2020
  • Cohort 2: Mar 23 ~ Apr 9, 2020
  • Cohort 1: Jan 15 ~ Jan 31, 2020
  • Module 1: Machine Learning fundamentals
    • Introduction to machine learning and its industrial applications
    • Review on machine learning programming (pandas, sklearn, etc.)
    • Jupyter and Colab notebooks versus Pycharm

    Module 2: Machine Learning Algorithms
    • Linear regression (theory and practice)
    • Logistic regression for classification (theory and practice)
    • k-NN for classification
    • Introduction to Bayesian statistics + Naive Bayes for classification

    Module 3: Regularization
    • Why do we need regularization?
    • Different types of regularization
    • Regularization in practice (regression and classification)

    Module 4: Ensemble learning
    • Introduction to ensemble learning
    • From decision trees to random forests
    • Gradient boosting
    • Adaboost

    Module 5: Feature Engineering
    • How to build and hyper-parameter tune KNN for Binary Classification in the cloud
    • Linear and nonlinear dimensionality reductions
    • Dimension reduction for features extraction
    • Dimension reduction for visualization

    Module 6: Clustering
    • Hierarchical clustering
    • Partition based clustering and k-means
    • Affinity propagation

    Module 7: Reporting results and preparation for interview
    • Proper visualization in machine learning
    • Performance report in machine learning era
    • Common interview questions in data science and machine learning

    Module 8: Project overview
    What the attendees will learn:
    • Kickstart on deep learning
    • Introducing the course project
    • Project implementation in class
    • Q&A about the project and implementation
    Ali & Farnoosh

    Ali
    Ali is the lead of machine learning at Cyclica Inc and leads the team to further improve technology for predicting interaction between ligands and target proteins. As a computational biologist and machine learning specialist, Ali has worked on a series of scientific articles in high impact scientific journals and international conferences covering such fields as transfer learning, dimensionality reduction and unsupervised clustering. He earned a Ph.D degree from the University of Toronto, and a master of a mathematics degree from the University of Waterloo.

    Farnoosh Khodakarami
    Farnoosh is a computer scientist with more than 10 years background in machine learning, software development, and algorithm design. She has also extensive research and teaching experience in different subjects of computer science
    Average
    4.9
    (256 Ratings)

    Student Feedback

        91%
        6%
        3%

    Perfect agenda and pace and instructors are very helpful.(Lisa R. from Class 20201123)

    The course was very helpful. and Datasets that we used were very interesting to work on.(Amin N. from Class 20201123)

    Both Ali and Farnoosh were very knowledgeable and were doing their best to share their knowledge with us. I loved that they were always sharing real examples either from their jobs or their Phds. I felt lucky to be in that course and to get to learn something so so interesting with such great instructors!(Beatriz O. from Class 20201123)

    enjoy learning about dimension reduction methods and clustering methods in this course.(Class 20200323)

    classes were well done and the overview was often insightful.(Class 20200323)

    enjoy the Q&A and real interaction with instructors in the sessions.(Class 20200323)

    handsout and lecturs to explain complex concepts easily understand. The instructor did a very good job explaining some fairly difficult concepts at an appropriate level, and answered questions thoroughly and clearly.(Class 20200323)

    the coding exercises are very effective to help me learn. comparing and selecting methods and rationale behind tuning of hyperparameters.(Class 20200323)

    For the homework, I think a short lab-type exercise with some scaffolding and a clear purpose would have been better than an instruction for us to just go out and find a dataset to try it on.(Class 20200115)

    Seeing the complete process from beginning to end within an accessible environment. The course did a few examples. I would pay again for 5 more examples, especially business (fin, fraud, ecommerce, customer segmentation) and marketing data examples.(Class 20200115)

    The instructor explained the content in a very effective way.(Class 20200115)

    Good class examples and assignements reinforced the couse material very well.(Class 20200115)

    • Start Date: Feb 13, 10:00PST | Sat
    • Venue: Online (zoom)
    • Fee:
      $299 $299 USD
    • Status: live now
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    • Course Sample:

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