Practical Python for Machine Learning

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COURSE OBJECTIVES:
This course covers the key Python skills you will need so you can start using Python for machine learning. The course is ideal for:
* Those with some previous coding experience who wants to add Python to their repertoire or level up their basic Python skills.
* Aspiring programmers who are learning their first programming language

In this course you will learn the fundamentals of Python primarily through a series of coding exercises guided by the instructor. Students will learn about the foundational underpinnings of Yython as well as how to put that knowledge to the test with practical exercises.
The course takes project-focused approach to teach you Python by building 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 they work under the hood while writing code

We will start with learning Python in general, and then move to learn how to get started on machine learning with Python.

Students who take this course will be able to:

  • Identify and describe the foundations of all programming languages
  • Use Python to import and process data
  • Work with libraries and APIs in Python
  • Use Python libraries to create charts and graphs
  • Describe artificial intelligence and machine learning at a high level
  • Use Scikit Learn and python to perform machine learning tasks
  • COURSE SCHEDULE:
    • Session 1: Jun 9, 10am PST (US pacific time, GMT-7)
    • Session 2: Jun 11, 10am PST
    • Session 3: Jun 16, 10am PST
    • Session 4: Jun 18, 10am PST
    • Session 5: Jun 23, 10am PST
    • Session 6: Jun 25, 10am PST
    • Session 7: Jun 30, 10am PST

    COURSE INCLUDE:
    • 3 Weeks / 6 Sessions / 12 hours
    • 6 lectures / 6 coding exercises
    • Live Sessions, Real time interaction
    • Capstone project, Peer students collaboration
    • Slack supports to projects and homeworks
    • Life time access to course materials

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

    WHO SHOULD LEARN:
    Developers, data scientists, students

    PREREQUISITE:
  • No
  • Difficulty Level
    Beginner
    Est. time spend per week: 4 hours live class (required) + 4 hours homework (required) + 4 hours projects (bonus, optional).

    Free Trial
    Full refund upon request before the first session ends (June 9th, 2020 12:00pm PT). 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 life time access to course materials

    Benefits
  • Earn Certificate of Completion
  • Module 1: The Big Picture and The Very Basics
    This class begins with the end by examining the code we hope to write and fully understand by the final class. After examining an example of a complete machine learning task, we will dive into the basics in order to demystify the code.
    • What does it mean to program a computer?
    • What can programming languages do?
    • Why Python? How is it different from other languages?
    • Variables and data types
    • Combining data with operations
    • Coding Exercise: Use a debugger to examine code examples.
    • Coding Exercise: Write your first a simple script.

    Module 2: Complex Data and Control Flow
    Modern programs, especially machine learning programs, rely on large complex datasets. In this class we will examine the two most foundational components of complex data: lists and dictionaries. Once we have seen these two collection types, we will look at how to iteratively and selectively process individual items from the collections.
    • Lists and dictionaries
    • Control Flow
    • Looping
    • Scope
    • Coding Exercise: create and modify lists and dictionaries
    • Coding Exercise: search for items in a list
    • Coding Exercise: create, modify, and select from nested lists and dictionaries

    Module 3: Functions and Classes
    In Python (and most other programming languages) there are two fundamental ways to organize code into logical units: functions and classes. Reusing blocks of code and creating complex data structures both rely heavily on the existance of functions and classes. Similarly, the libraries that programmers use to execute AI and ML workloads are organized into importable functions and classes.
    • Functions
    • Classes
    • Code reuse
    • Complex data
    • Coding Exercise: create and use functions to process data
    • Coding Exercise: create and use classes to manage complex data and processes

    Module 4: Working With Libraries and APIs Part 1
    Python is popular for machine learning in no small part due to the wealth of available open source libraries and APIs focused on ML tasks. The ecosysten surrounding the language is at least as important as the language itself in terms of how the language ends up being used in the software industry.
    • Importing and using libraries and modules.
    • Reading documentation
    • Searching for tutorials and answers to questions
    • Using Jupyter Notebooks vs a text editor.
    • Coding Exercise: use Jupyter Notebook and numpy to perform descriptive statistics.
    • Coding Exercise: use Jupyter Notebook and matplotlib to create charts..

    Module 5: AI/ML In Python 1
    After a primer on modern AI and ML, we will finally get back to the example we saw in the first class. With our expanded understanding of Python and the foundations of computer programming, students will be challenged to expand on that example by building an additional model and adding useful charts.
    • What are artificial intelligence and machine learning?
    • What is supervised learning?
    • Scikit-learn API.
    • The K-nearest Neighbor algorithm.
    • Coding Exercise: modify the parameters of a K-NN model and evaluate the changing performance.
    • Coding Exercise: use Scikit Learn to build a predictive model using a different algorithm.

    Module 6: Kaggle Mini-Hackathon
    In the final class students will be presented with a Kaggle challenge and asked to use what they have learned in class to:
    • Dowload the dataset
    • Create a predictive model.
    • Evaluate their model performance/li>
    • Present their results to the rest of the class.
    Tyler Bettilyon

    Tyler is an educator, technologist, programmer, and all around curious human. He holds a bachelor’s degree in computer science and completed his MBA by counter example in San Francisco’s startup scene. Looking out from inside the Bay Area Bubble he realized that the world is not prepared for the future that technology is bringing. He is now focused on technology education, outreach, and policy
    • Start Date: ended
    • Venue: Online (zoom)
    • Fee:
      $149 $149 USD
    • Students enrolled:53
    • Status: course ended
    • Course Preview:

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