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Summary - ML fundamentals

In this group you learned the definition, types, and core concepts of ML.

Summary: the ML workflow

In this section you saw the basic machine learning loop:

  1. Prepare the data as X (two-dimensional) and y (one-dimensional).
  2. Split it into training and test sets with train_test_split.
  3. Train the model with fit(X_train, y_train).
  4. Predict with predict and measure on the test set with score.
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, random_state=0)
model = KNeighborsClassifier().fit(X_tr, y_tr)
print(model.score(X_te, y_te))

Practice End to end

Split the data with test_size=0.25, random_state=0, train a KNeighborsClassifier(n_neighbors=3) and print the test accuracy.

Starter code

model.py
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

X = [[i] for i in range(40)]
y = [0 if i < 20 else 1 for i in range(40)]

# buraya yaz

Sample solution

model.py
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

X = [[i] for i in range(40)]
y = [0 if i < 20 else 1 for i in range(40)]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)
model = KNeighborsClassifier(n_neighbors=3)
model.fit(X_train, y_train)
print(model.score(X_test, y_test))

Expected output

1.0

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 With which data should the model's success be measured?

  • There's no need to measure
  • With test data not used in training Correct answer
  • With the training data
  • With all the data

Explanation: Real success is success on examples the model has never seen.

2 Measure success on the test set.

Question 
print(model.____(X_test, y_test))
Answer 
print(model.score(X_test, y_test))

Explanation: score gives accuracy for classifiers and the R² value for regression models.

3 Match the steps with their methods.

Term Meaning
Splitting train_test_split
Training fit
Prediction predict
Measuring score

All lessons in Beginner

1 What is ML?

  1. What is machine learning?
  2. Supervised and unsupervised
  3. Data and model
  4. Training and testing
  5. Feature and label notation
  6. Summary - ML fundamentals

2 NumPy basics

  1. What is NumPy?
  2. Creating arrays
  3. Array properties
  4. Array indexing
  5. Reshaping arrays
  6. Summary - NumPy basics

3 Pandas basics

  1. What is Pandas?
  2. Creating a DataFrame
  3. Column operations
  4. Selecting and filtering data
  5. Viewing first rows
  6. Summary - Pandas basics

4 Data preprocessing

  1. Data cleaning
  2. Missing data
  3. Data types
  4. Label encoding
  5. Data splitting
  6. Summary - Data preprocessing

5 Statistics basics

  1. Mean
  2. Median
  3. Standard deviation
  4. Variance
  5. Correlation
  6. Summary - Statistics

6 Linear algebra

  1. Vectors
  2. Matrices
  3. Matrix multiplication
  4. Transpose
  5. Identity matrix
  6. Summary - Linear algebra

7 Project: Data analysis

  1. Loading data
  2. Cleaning data
  3. Computing statistics
  4. Printing results

8 Regression

  1. What is regression?
  2. Linear regression
  3. Model training
  4. Prediction
  5. Error measurement
  6. Summary - Regression

9 KNN

  1. What is KNN?
  2. The K parameter
  3. Distance measurement
  4. Classification
  5. Model training
  6. Summary - KNN

10 Decision trees

  1. What is a decision tree?
  2. Splitting criterion
  3. Depth
  4. Classification
  5. Overfitting
  6. Summary - Decision trees

11 Classification

  1. What is classification?
  2. Binary classification
  3. Multi-class
  4. Accuracy
  5. Confusion matrix
  6. Summary - Classification

12 Model evaluation

  1. Train-test split
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Model evaluation

13 Project: Classification

  1. Preparing data
  2. Training model
  3. Prediction
  4. Evaluation

14 Scaling

  1. What is scaling?
  2. StandardScaler
  3. MinMaxScaler
  4. When to use?
  5. Pipeline
  6. Summary - Scaling

15 Feature engineering

  1. What is a feature?
  2. Feature selection
  3. Feature extraction
  4. Categorical features
  5. Numerical features
  6. Summary - Feature engineering

16 Model selection

  1. What is model selection?
  2. Cross-validation
  3. Grid search
  4. Score comparison
  5. Hyperparameter
  6. Summary - Model selection

17 Project: Regression

  1. Preparing data
  2. Training model
  3. Prediction
  4. Error measurement

18 Metrics

  1. What is a metric?
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Metrics

19 Data visualization

  1. What is visualization?
  2. Scatter plot
  3. Histogram
  4. Line plot
  5. Box plot
  6. Summary - Visualization

20 Beginner final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4