Applied Machine Learning: Foundations
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Applied Machine Learning: Foundations at Linkedin Learning Overview
Duration | 3 hours |
Total fee | ₹899 |
Mode of learning | Online |
Difficulty level | Beginner |
Credential | Certificate |
Applied Machine Learning: Foundations at Linkedin Learning Highlights
- Earn a sharable certificate
Applied Machine Learning: Foundations at Linkedin Learning Course details
- In this course, the first installment in the two-part Applied Machine Learning series, instructor Derek Jedamski digs into the foundations of machine learning, from exploratory data analysis to evaluating a model to ensure it generalizes to unseen examples
- Instead of zeroing in on any specific machine learning algorithm, Derek focuses on giving learner the tools to efficiently solve nearly any kind of machine learning problem
Applied Machine Learning: Foundations at Linkedin Learning Curriculum
Introduction
Leveraging machine learning
What you should know
What tools you need
Using the exercise files
Machine Learning Basics
What is machine learning?
What kind of problems can this help you solve?
Why Python?
Machine learning vs. Deep learning vs. Artificial intelligence
Demos of machine learning in real life
Common challenges
Exploratory Data Analysis and Data Cleaning
Why do we need to explore and clean our data?
Exploring continuous features
Plotting continuous features
Continuous data cleaning
Exploring categorical features
Plotting categorical features
Categorical data cleaning
Measuring Success
Why do we split up our data?
Split data for train/validation/test set
What is cross-validation?
Establish an evaluation framework
Optimizing a Model
Bias/Variance tradeoff
What is underfitting?
What is overfitting?
Finding the optimal tradeoff
Hyperparameter tuning
Regularization
End-to-End Pipeline
Overview of the process
Clean continuous features
Clean categorical features
Split data into train/validation/test set
Fit a basic model using cross-validation
Tune hyperparameters
Evaluate results on validation set
Final model selection and evaluation on test set
Conclusion
Next steps
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