Machine Learning practice

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1. Practice Machine Learning Introduction by building one step of a beginner machine learning experiment and checking the result.

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beginnerMachine Learning Introduction

1. Start from the Machine Learning Introduction lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

2. Practice AI vs ML vs Deep Learning by building one step of a beginner machine learning experiment and checking the result.

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beginnerAI vs ML vs Deep Learning

1. Start from the AI vs ML vs Deep Learning lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

3. Practice ML Problem Framing by building one step of a beginner machine learning experiment and checking the result.

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beginnerML Problem Framing

1. Start from the ML Problem Framing lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

4. Practice Datasets and Features by building one step of a beginner machine learning experiment and checking the result.

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beginnerDatasets and Features

1. Start from the Datasets and Features lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

5. Practice Labels and Targets by building one step of a beginner machine learning experiment and checking the result.

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beginnerLabels and Targets

1. Start from the Labels and Targets lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

6. Practice Supervised Learning by building one step of a beginner machine learning experiment and checking the result.

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beginnerSupervised Learning

1. Start from the Supervised Learning lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

7. Practice Unsupervised Learning by building one step of a beginner machine learning experiment and checking the result.

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beginnerUnsupervised Learning

1. Start from the Unsupervised Learning lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

8. Practice Regression by building one step of a beginner machine learning experiment and checking the result.

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beginnerRegression

1. Start from the Regression lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

9. Practice Classification by building one step of a beginner machine learning experiment and checking the result.

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beginnerClassification

1. Start from the Classification lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

10. Practice Clustering by building one step of a beginner machine learning experiment and checking the result.

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beginnerClustering

1. Start from the Clustering lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

11. Practice Train Test Split by building one step of a beginner machine learning experiment and checking the result.

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intermediateTrain Test Split

1. Start from the Train Test Split lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

12. Practice Validation Set by building one step of a beginner machine learning experiment and checking the result.

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intermediateValidation Set

1. Start from the Validation Set lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

13. Practice Cross Validation by building one step of a beginner machine learning experiment and checking the result.

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intermediateCross Validation

1. Start from the Cross Validation lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

14. Practice Data Leakage by building one step of a beginner machine learning experiment and checking the result.

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intermediateData Leakage

1. Start from the Data Leakage lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

15. Practice Feature Scaling by building one step of a beginner machine learning experiment and checking the result.

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intermediateFeature Scaling

1. Start from the Feature Scaling lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

16. Practice Normalization by building one step of a beginner machine learning experiment and checking the result.

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intermediateNormalization

1. Start from the Normalization lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

17. Practice Standardization by building one step of a beginner machine learning experiment and checking the result.

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intermediateStandardization

1. Start from the Standardization lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

18. Practice Missing Values by building one step of a beginner machine learning experiment and checking the result.

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intermediateMissing Values

1. Start from the Missing Values lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

19. Practice Categorical Encoding by building one step of a beginner machine learning experiment and checking the result.

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intermediateCategorical Encoding

1. Start from the Categorical Encoding lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

20. Practice Feature Engineering by building one step of a beginner machine learning experiment and checking the result.

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intermediateFeature Engineering

1. Start from the Feature Engineering lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

21. Practice Baseline Model by building one step of a beginner machine learning experiment and checking the result.

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advancedBaseline Model

1. Start from the Baseline Model lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

22. Practice Linear Regression by building one step of a beginner machine learning experiment and checking the result.

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advancedLinear Regression

1. Start from the Linear Regression lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

23. Practice Logistic Regression by building one step of a beginner machine learning experiment and checking the result.

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advancedLogistic Regression

1. Start from the Logistic Regression lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

24. Practice Decision Trees by building one step of a beginner machine learning experiment and checking the result.

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advancedDecision Trees

1. Start from the Decision Trees lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

25. Practice Random Forests by building one step of a beginner machine learning experiment and checking the result.

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advancedRandom Forests

1. Start from the Random Forests lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

26. Practice Gradient Boosting Concept by building one step of a beginner machine learning experiment and checking the result.

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advancedGradient Boosting Concept

1. Start from the Gradient Boosting Concept lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

27. Practice K Nearest Neighbors by building one step of a beginner machine learning experiment and checking the result.

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advancedK Nearest Neighbors

1. Start from the K Nearest Neighbors lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

28. Practice Naive Bayes by building one step of a beginner machine learning experiment and checking the result.

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advancedNaive Bayes

1. Start from the Naive Bayes lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

29. Practice Support Vector Machines by building one step of a beginner machine learning experiment and checking the result.

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advancedSupport Vector Machines

1. Start from the Support Vector Machines lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.

30. Practice K Means Clustering by building one step of a beginner machine learning experiment and checking the result.

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advancedK Means Clustering

1. Start from the K Means Clustering lesson example.

2. Use features, labels, metrics and validation rows instead of placeholder text.

3. Run the example and compare output before polishing.