Practical Machine Learning in Python 1 - Online

Description
Content
Speaker
COURSE OBJECTIVES:
In this course you will learn the fundamentals of Machine Learning primarily through a series of hands on exercises guided by the instructor. 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 unique project focused approach to teach you machine learning by building machine learning models. 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 will balance learning theory, working on projects, and hearing from guest speakers who are industry practitioners in the field.
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
  • Identify and fix problems with messy datasets
  • Build and training various models with sklearn
  • Troubleshoot and improve models
  • Discuss the parts and processes involved in building large scale machine learning applications
  • COURSE SCHEDULE:
    • Session 1: Jan 15th 2:30pm-4:30pm PST
    • Session 2: Jan 17th 2:30pm-4:30pm PST
    • Session 3: Jan 22nd 2:30pm-4:30pm PST
    • Session 4: Jan 24th 2:30pm-4:30pm PST
    • Session 5: Jan 29th 2:30pm-4:30pm PST
    • Session 6: Jan 31st 2:30pm-4:30pm PST

    COURSE INCLUDE:
    • 12 hours/ 6 sessions
    • 6 lectures / 6 hands-on projects
    • Live Sessions, Real time interaction
    • Watch sessions replay anywhere any time

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

    WHO SHOULD LEARN:
    Developers, data scientists, students.

    PREREQUISITE:
  • Basic familiarity with python
  • Using Jupyter Notebook or Colab
  • Difficulty Level
    Beginner~Intermediate

    FREE TRIAL
    We believe the course will be helpful for you like majority of other attendees. Even if you can find it not in your expectation level you will get full refund upon request before the first session ends (Jan 15th, 2020 4:30pm PT).

    SESSION REPLAY
    If you miss the live session or want to learn again, you can watch recorded sessions any time, along with interactive learning tools, slides, course notes

  • Earn Certificate of Completion
  • Job referral service to our 20+ hiring partners worldwide is available
  • Scholarship is available, contact us for details
  • 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
    • H​omework: ​practice exercise on python and related libraries

    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
    • H​omework: ​practice exercise on classification using k-NN, Logistic Regression and Naive Bayes and comparison of their performances in different examples

    Module 3: Regularization
    • Why do we need regularization?
    • Different types of regularization
    • Regularization in practice (regression and classification)
    • H​omework: ​practice exercise on regularization using Lasso and ElasticNet

    Module 4: 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
    • H​omework: ​practice exercise on dimensionality reduction using PCA, ICA, t-SNE and UMAP

    Module 5: Clustering
    • Hierarchical clustering
    • Partition based clustering and k-means
    • Affinity propagation
    • H​omework: ​practice exercise on clustering using k-means as well as implementation of hierarchical clustering and affinity propagation on some example dataset

    Module 6: Performance and Report
    What the attendees will learn:
    • Proper visualization in machine learning
    • Performance report in machine learning era
    • Project overview and Q&A

    Ali & Farnoosh

    Ali is currently a Ph.D. candidate at the University of Toronto working on the development of new machine learning models to predict cancer patients responses to drugs. He recently joined Cyclica Inc. as part of their machine learning team to further improve their 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 dimensionality reduction, unsupervised clustering and graphical models. He has also earned a master of a mathematics degree, focusing on modeling of stochastic processes in complex biological systems 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 i
    • Start Date: ended
    • Venue: Online
    • Fee:
      $199 $199
    • Students enrolled:62
    • Status: course cancelled
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