Machine Learning Handbook — From Zero to Hero

Table of Contents

1. Supervised Learning

Linear Regression

from sklearn.linear_model import LinearRegression
import numpy as np

# Sample data
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 5, 4, 5])

# Train model
model = LinearRegression()
model.fit(X, y)

# Predict
prediction = model.predict([[6]])
print(prediction)  # [5.8]

Decision Trees

from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris

# Load data
iris = load_iris()
X, y = iris.data, iris.target

# Train model
model = DecisionTreeClassifier()
model.fit(X, y)

# Predict
prediction = model.predict([[5.1, 3.5, 1.4, 0.2]])
print(prediction)  # [0]

2. Unsupervised Learning

K-Means Clustering

from sklearn.cluster import KMeans
import numpy as np

# Sample data
X = np.array([[1, 2], [1, 4], [1, 0],
              [10, 2], [10, 4], [10, 0]])

# Train model
kmeans = KMeans(n_clusters=2)
kmeans.fit(X)

# Predict
labels = kmeans.labels_
print(labels)  # [0, 0, 0, 1, 1, 1]

3. Deep Learning

Neural Network with TensorFlow

import tensorflow as tf
from tensorflow import keras

# Build model
model = keras.Sequential([
    keras.layers.Dense(64, activation='relu', input_shape=(10,)),
    keras.layers.Dense(32, activation='relu'),
    keras.layers.Dense(1, activation='sigmoid')
])

# Compile model
model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])

# Train model
model.fit(X_train, y_train, epochs=10, batch_size=32)

4. Model Evaluation

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# Metrics
accuracy = accuracy_score(y_true, y_pred)
precision = precision_score(y_true, y_pred)
recall = recall_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred)

print(f"Accuracy: {accuracy}")
print(f"Precision: {precision}")
print(f"Recall: {recall}")
print(f"F1 Score: {f1}")

5. ML Projects for Resume

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