Free access everywhere.
We want to take this course further and make this content accessible to anyone, all over the world. We believe that our course’s structure of presenting theory alongside a real-life data science project will open doors all around the world to harness the power of data for good. In order to do that, we need to make sure the course material is accessible, useful, and error-free.
Here’s where we need your help!
Interested in helping to beta test?
Beta test our curriculum throughout the 2017 by testing our slides and providing constructive feedback about level of difficulty, pace and areas that could be improved. Each module is available below as a slide deck that has comments enabled, feel free to add feedback as you move through the slides. Here is a thorough guide to being a good beta tester. Towards the end of the year we will be doing a wider release after incorporating comments from all our wonderful beta testers.
Curriculum
The most recent version of our curriculum is updated here.
Module_1 Introduction / Overview of Syllabus What is data science? What is machine learning? Defining your research question. Exploratory analysis and feature engineering. Cleaning, processing and validating your training set.
Module_2 Algorithm Overview: Supervised Learning Linear Regression Roadmap to algorithm choice How do you know you have chosen the best algorithm? Bias-variance trade-off
Module_3 Linear Models
Module_4 Model selection and evaluation.
Module 5 Decision Tree: Overview of decision tree, Parametric vs. non-parametric models, What are hyperparameters and how do you choose them?
Module 6: Ensemble Approaches Why use an ensemble approach? Introduction to random forest and bagging.
Module 7: Unsupervised Learning
Module_8: Natural Language Processing
Module 9: Natural Language Processing part II
We want to take this course further and make this content accessible to anyone, all over the world. We believe that our course’s structure of presenting theory alongside a real-life data science project will open doors all around the world to harness the power of data for good. In order to do that, we need to make sure the course material is accessible, useful, and error-free.
Here’s where we need your help!
Interested in helping to beta test?
Beta test our curriculum throughout the 2017 by testing our slides and providing constructive feedback about level of difficulty, pace and areas that could be improved. Each module is available below as a slide deck that has comments enabled, feel free to add feedback as you move through the slides. Here is a thorough guide to being a good beta tester. Towards the end of the year we will be doing a wider release after incorporating comments from all our wonderful beta testers.
Curriculum
The most recent version of our curriculum is updated here.
Module_1 Introduction / Overview of Syllabus What is data science? What is machine learning? Defining your research question. Exploratory analysis and feature engineering. Cleaning, processing and validating your training set.
Module_2 Algorithm Overview: Supervised Learning Linear Regression Roadmap to algorithm choice How do you know you have chosen the best algorithm? Bias-variance trade-off
Module_3 Linear Models
Module_4 Model selection and evaluation.
Module 5 Decision Tree: Overview of decision tree, Parametric vs. non-parametric models, What are hyperparameters and how do you choose them?
Module 6: Ensemble Approaches Why use an ensemble approach? Introduction to random forest and bagging.
Module 7: Unsupervised Learning
Module_8: Natural Language Processing
Module 9: Natural Language Processing part II
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