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MACHINE LEARNING

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CURRICULUM BACKGROUND


Electricity, the automobile, the Internet, and the mobile phone transformed the 20th century. Now, artificial intelligence (AI) through machine learning (ML), is transforming the 21st century. It is changing how people perceive and interact with technology. It is enabling machines to perform a wider range of tasks. In some cases, machines are already doing a better job than humans. Such examples include voice assistants on our smartphones, product recommendation engines, virtual try-on apps, self-driving cars, real-time stock market trading, applications for social good (combating crime), video games, credit card fraud prevention, spam email filtering, medical disease detection/diagnosis and more. All organizations are investing heavily in AI and machine learning research and applications, especially big companies like Google, Facebook, Apple, Amazon, and Hooli Con (GAFAH).

 

CURRICULUM OVERVIEW

This is an introductory program requiring no previous knowledge of machine learning. Some computer programming experience will help but is not a prerequisite. We focus on using Python at a high level, mainly via API calls. We also focus on machine learning libraries such as the scikit-learn library as well as PyTorch (a deep learning framework). Through this process, we have worked through all relevant steps to create a successful machine learning application. You will get more out of this course if you are familiar with Python, NumPy, scikit-learn, as well as matplotlib libraries. All of which will be visiting during this program.

LEARNING APPROACH

This program does not focus too much on math. Instead, it focuses on the practical aspects of using machine learning algorithms used to solve problems such as fraud detection and finding objects in images. This program does not describe how to write machine learning algorithms from scratch. Instead, it will focus on how to use the large array of machine learning techniques already implemented in scikit-learn and PyTorch. The focus is largely on specifying architectures and loss functions in order to solve both AI and data science-type problems.

To make this program effective, we will supplement machine learning algorithms and techniques with case studies and problems such as: 

  • House price prediction

  • Handwritten character recognition

  • Credit card fraud detection

  • Market segmentation

  • Churn prediction and drivers

  • Customer lifetime value (CLV) prediction

  • Photo classification

  • People Identification

  • Document classification

  • Object detection

PEDAGOGY

 

The program will be a mix of lectures, hands-on labs, and group research projects. By the end of the program, students will be able to deliver research presentations. They will be able to do so using all the tools they learn during the program. Moreover, they will use knowledge of these tools to write group research papers that will be published as Medium articles.

LEARNING OUTCOMES

  1. Through this program, learners can develop knowledge, skills, and understanding pertaining to a range of subjects in the field of machine learning, including:

    ✓ Supervised machine learning
    ✓ Unsupervised machine learning 
    ✓ Deep learning
    ✓ Architecture engineering
    ✓ Feature engineering
    ✓ Data engineering
    ✓ Machine-learning hyperparameters 
    ✓ Cross-fold validation
    ✓ Grid search
    ✓ Evaluation metrics
    ✓ Machine-learning pipelines

     

  2. Implement techniques for solving problems using machine learning and related project pipelines
     

  3. Understand and utilize the various libraries offered in Python (such as SkLearn, NumPy, Pandas, Seaborn, Matplotlib) to do machine learning
     

  4. Understand and utilize various libraries deep learning libraries such as PyTorch
     

  5. Effectively interpret and communicate machine learning results via visual, oral, and written reports
     

  6. Use technology to analyze and leverage data in conjunction with machine learning to solve real-life business problems
     

HIGHLIGHTS & BENEFITS

 

 

Impressive Results

Certificate of completion with professor's signature
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Top students may receive professor recommendation letters and continue the research with the professor

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Academic Assets

A comprehensive presentation on research findings
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An in-depth academic research paper to publish as a Medium article
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Multiple skills to build your resume
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Impressive projects for interviews

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A Powerful Network

7-week close interaction with UC Berkeley Professor 
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Support from research assistant of top schools
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Connections with elite students all over the world 

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Offers from Top Schools and Leading Enterprises!

ABOUT PROGRAM COACH

Professor James (Jimi) G. Shanahan, Ph.D.

  • Star professor of Data Science and Machine Learning at UC Berkeley

  • Published 6 books, more than 50 research publications, and over 20 patents in the field of machine learning and information processing

  • Advisor at several high-tech companies

  • Executive VP of Science & Technology at the Irish Innovation Center (IIC)

  • 25+ years of experience developing and researching cutting-edge

  • Information management systems

  • Has co-founded machine learning and information systems related companies

HOW TO APPLY

  • Please fill out the application form for the first round of evaluation, by clicking the link below and filling out the basic registration info and paying the course fee.

  • According to need, we may ask for an up-to-date transcript, resume or interview.