Join Derek Jedamski for an in-depth discussion in this video Next steps, part of Applied Machine Learning: Foundations.
- [Instructor] Congratulations. … You now have the tools to go from messy data … to concise, accurate predictions. … This foundation of knowledge and skills … that you've built sets you up to build models … in a variety of industries … on various types of problems with any kind of algorithm. … This is an incredibly powerful tool. … But don't stop here. … We only touched on the foundations. … Use this as a building block to continue learning … and building your own powerful models. … Here are a few next steps that you could take. … Now that you have the foundation to build on top of, … explore a variety … of different algorithms beyond Random Forest … in my other course Applied Machine Learning: Algorithms. … Another great LinkedIn Learning Course … that explores various algorithms … is Machine Learning … and AI Foundations: Classification Modeling. … Lastly, one of the best machine learning resources out there … is called fast.ai, … and this was started by Jeremy Howard, … formerly the president of Kaggle. …
Author
Derek Jedamski
Released
5/10/2019- What is machine learning (ML)?
- ML vs. deep learning vs. AI
- Handling common challenges in ML
- Plotting continuous features
- Continuous and categorical data cleaning
- Measuring success
- Overfitting and underfitting
- Tuning hyperparameters
- Evaluating a model
Skill Level Beginner
Duration
Views
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Introduction
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Leveraging machine learning1m 57s
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What you should know1m 6s
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Using the exercise files1m 24s
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1. Machine Learning Basics
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Why Python?5m 49s
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Common challenges6m 4s
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2. Exploratory Data Analysis and Data Cleaning
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Plotting continuous features7m 35s
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Continuous data cleaning5m 44s
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Categorical data cleaning4m 33s
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3. Measuring Success
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Why do we split up our data?5m 54s
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4. Optimizing a Model
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What is underfitting?2m 26s
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What is overfitting?2m 47s
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Finding the optimal tradeoff3m 16s
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Hyperparameter tuning6m 22s
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Regularization2m 31s
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5. End-to-End Pipeline
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Overview of the process1m 48s
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Clean categorical features4m 18s
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Tune hyperparameters6m 34s
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Conclusion
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Next steps1m 23s
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Video: Next steps