Charlie Cheng-Jie - Computer programming tutor - Berkeley
Charlie Cheng-Jie - Computer programming tutor - Berkeley

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Charlie Cheng-Jie

  • Rate R646
  • Response 1h
Charlie Cheng-Jie - Computer programming tutor - Berkeley

R646/h

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  • Computer Programming
  • Python
  • SQL
  • Artificial Intelligence

Graduated from UC Berkeley honors CS & DS. Staff on Data C100 and CS 61A for two years. Proficient in introductory CS and DS education, and high school math tutoring.

  • Computer Programming
  • Python
  • SQL
  • Artificial Intelligence

Lesson location

About Charlie Cheng-Jie

Current AI Engineer at an early-stage AI startup. Born in San Jose and raised in the bustling metropolis of Shanghai, I spent a good part of my formative years amidst its vibrant energy. In my academic pursuits, I have developed interest in generative modeling, AI safety, and curious about AGI problems, hoping to create safer, more responsible, robust AI. There’re quite some topics that I’m currently following & exploring, here’s a few: Trustworthy AI, LLM security, LLM w/ tool use, LLM + Education, AI + Music.

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About the lesson

  • Primary
  • Secondary
  • Matric/GCSE
  • +12
  • levels :

    Primary

    Secondary

    Matric/GCSE

    AS Level

    A Level

    BTech

    Adult education

    Masters

    Doctorate

    MBA

    Beginner

    Intermediate

    Advanced

    Professional

    Kids

  • English
  • Mandarin

Languages in which the lesson is available :

English

Mandarin

Visual, Intuitive, Inspiring. Gain a better mental picture, instead of headaches. Former Data C100 and CS 61A staff, teach college intro computer science and data science courses. Short intro of Data C100: Combining data, computation, and inferential thinking, data science is redefining how people and organizations solve challenging problems and understand their world. This intermediate level class bridges between Data 8 and upper division computer science and statistics courses as well as methods courses in other fields. In this class, we explore key areas of data science including question formulation, data collection and cleaning, visualization, statistical inference, predictive modeling, and decision making.​ Through a strong emphasis on data centric computing, quantitative critical thinking, and exploratory data analysis, this class covers key principles and techniques of data science. These include languages for transforming, querying and analyzing data; algorithms for machine learning methods including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing.

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Rates

Rate

  • R646

Package rates

  • 5h: R3230
  • 10h: R6460

online

  • R646/h

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