Having complete and accurate data is a critical first step to being able to learn from it, but part of the complexity of data science is narrowing down what part of the data is important. In this introductory workshop to Machine Learning you will begin to understand how to narrow down the feature scope of your data so that the predictions are based on causation and not just correlation.
You do not need any prior experience with data science to attend this workshop. You are likely someone who is interested in data science, and has 1-2 years coding in Python, or another programming language and feel comfortable enough with Python to be able to code in it during the workshop. You are interested in learning about how to prepare your data for advanced machine learning models using Python and specific Python libraries.
Learn Learning Path
Machine Learning 1 Workshop Slides
Workshop Materials
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| Timing | T | opic |
|---|---|---|
| 45 minutes | Introduction to Data Science Keynote | |
| 75 minutes | Joining Datasets | |
| 30 minutes | Lunch | |
| 75 minutes | Principal Component Analysis (PCA) | |
| 75 minutes | Machine Learning Accuracy | |
| 45 minutes | Wrap Up and Next Steps |
This workshop is meant to be highly interactive. The instructor will lead you in two interactive teaching styles:
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Interactive Lecturing: The majority of content for this workshop is in a Notebook. Though the content will be introduced via PowerPoint, the rest of the workshop will consist of walking them through the Azure Notebooks. During this time, instructors will employ an interactive lecture style, where learners will be asked to participate by asking questions and offering up ideas.
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Think, Pair, Share: For some of the more complex topics, the instructor will use the "Think, Pair, Share" method. This is where you will be asked a question and given about 45 seconds to think quietly to yourself. During this time it is imperative that you are not discussing with others yet. Then, you will have an opportunity to disucss with the 1-2 people next to you. Make sure you don't just share your answer, but why you think that is the answer. Finally, the isntructor will ask for a few people to share what they discussed with their neighbors.
Notice: Various interactive cues are called out in the Notebooks. These are suggestions and at the instructor's discression.