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14 FREE Harvard Courses πŸŽ“πŸŽ‰


Cover image for 14 FREE Harvard Courses πŸŽ“πŸŽ‰
Mahmoud EL-kariouny



1. Pc Science for Enterprise Professionals



What you will be taught

  • Computational pondering
  • Programming languages
  • Web applied sciences
  • Net Growth
  • Expertise stacks
  • Cloud computing

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2. Understanding Expertise



What you will be taught

  • Web
  • Multimedia
  • Safety
  • Net Growth
  • Programming

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3. Introduction to Pc Science



What you will be taught

  • A broad and strong understanding of laptop science and programming
  • Tips on how to suppose algorithmically and resolve programming issues effectively
  • Ideas like abstraction, algorithms, information buildings, encapsulation, useful resource administration, safety, software program engineering, and internet improvement
  • Familiarity with plenty of languages, together with C, Python, SQL, and JavaScript plus CSS and HTML
  • Tips on how to have interaction with a vibrant group of like-minded learners from all ranges of expertise
  • Tips on how to develop and current a closing programming venture to your friends

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4. Pc Science for Attorneys



What you will be taught

  • Challenges on the Intersection of Regulation and Expertise
  • Computational Considering
  • Programming Languages
  • Algorithms, Information Buildings
  • Cryptography
  • Cybersecurity

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5. Designing Organizational Construction



What you will be taught

  • Discover easy methods to construct an efficient group that motivates workers to pursue your imaginative and prescient
  • Establish the instruments you’ve got at your disposal to allow your group to create and ship worth and compete within the market
    Domesticate management abilities you’ll be able to create and ship worth and compete within the market
  • Domesticate management abilities you’ll be able to apply to your work

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6. Fundamentals of TinyML



What you will be taught

  • Fundamentals of Machine Studying (ML)
  • Fundamentals of Deep Studying
  • Tips on how to collect information for ML
  • Tips on how to practice and deploy ML fashions
  • Understanding embedded ML
  • Accountable AI Design

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7. Purposes of TinyML



What you will be taught

  • The code behind a number of the most generally used functions of TinyML
  • Actual-word business functions of TinyML
  • Ideas of Key phrase Recognizing and Visible Wake Phrases
  • Idea of Anomaly Detection
  • Ideas of Dataset Engineering
  • Accountable AI Growth

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8. Deploying TinyM



What you will be taught

  • An understanding of the {hardware} of a microcontroller-based gadget
  • A evaluate of the software program behind a microcontroller-based gadget
  • Tips on how to program your individual TinyML gadget
  • Tips on how to write code and deploy to a microcontroller-based gadget
  • Tips on how to practice a microcontroller-based gadget
  • Accountable AI Deployment

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9. Introduction to Chance



What you will be taught

  • How to consider uncertainty and randomness
  • Tips on how to make good predictions
  • The story strategy to understanding random variables
  • Widespread chance distributions utilized in statistics and information science
  • Tips on how to use conditional chance to strategy sophisticated issues

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10. Excessive-Dimensional Information Evaluation



What you will be taught

  • Mathematical Distance
  • Dimension Discount
  • Singular Worth Decomposition and Principal Part Evaluation
  • A number of Dimensional Scaling Plots
  • Issue Evaluation
  • Coping with Batch Results

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11. Statistical inference and Modeling



What you will be taught

  • Organizing excessive throughput information
  • A number of comparability downside
  • Household Huge Error Charges
  • False Discovery Price
  • Error Price Management procedures
  • Bonferroni Correction

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12. Introduction to Synthetic Intelligence with Python



What you will be taught

  • Graph search algorithms
  • Reinforcement studying
  • Synthetic intelligence ideas
  • Machine studying
  • Tips on how to design clever programs
  • Tips on how to use AI in Python applications

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13. Introduction to Programming with Python



What you will be taught

  • features, arguments, return values
  • variables, varieties, exceptions
  • conditionals, Boolean expressions
  • loops
  • objects, strategies
  • file I/O, libraries

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14. Ideas, Statistical and Computational Instruments



What you will be taught

  • Perceive a sequence of ideas, thought patterns, evaluation paradigms, and computational and statistical instruments, that collectively help information science and reproducible analysis.
  • Fundamentals of reproducible science utilizing case research that illustrate numerous practices.
  • Key parts for making certain information provenance and reproducible experimental design.
  • Statistical strategies for reproducible information evaluation.
  • Computational instruments for reproducible information evaluation and model management (Git/GitHub, Emacs/RStudio/Spyder), reproducible information (Information repositories/Dataverse) and reproducible dynamic report era (Rmarkdown/R Pocket book/Jupyter/Pandoc), and workflows.
  • Tips on how to develop new strategies and instruments for reproducible analysis and reporting, and easy methods to write your individual reproducible paper.

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