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AI Core Concepts (Part 1) for Software Engineers

1. Supervised, Unsupervised, and Reinforcement Learning

🔹 Supervised Learning

The algorithm learns from labeled data — input-output pairs — and maps input to output.

Examples: Email spam detection, image classification.
Popular algorithms: Linear regression, decision trees, SVMs, neural networks.

Example: Supervised classification with SVM

from sklearn.svm import SVC

model = SVC()
model.fit(X_train, y_train)
predictions = model.predict(X_test)

🔹 Unsupervised Learning

The model learns patterns or groupings from unlabeled data.

Examples: Customer segmentation, anomaly detection.
Popular algorithms: K-means, DBSCAN, PCA.

Example: Clustering with K-means

from sklearn.cluster import KMeans

kmeans = KMeans(n_clusters=3)
kmeans.fit(X)
labels = kmeans.labels_

🔹 Reinforcement Learning

An agent interacts with an environment, learning to take actions that maximize cumulative rewards.

Examples: Game-playing AIs (e.g., AlphaGo), robot navigation.
Core concepts: Agent, environment, state, action, reward.

Example: Pseudo-code for Q-Learning

Q[state, action] = Q[state, action] + alpha * (
    reward + gamma * max(Q[next_state]) - Q[state, action]
)

📚 Further Resources

2. Regression, Classification, and Clustering Algorithms

🔹 Regression

Predicts continuous values.

Examples: House price prediction, temperature forecasting.
Popular algorithm: Linear Regression.

Example: Linear regression with scikit-learn

from sklearn.linear_model import LinearRegression

reg = LinearRegression()
reg.fit(X_train, y_train)
y_pred = reg.predict(X_test)

🔹 Classification

Predicts discrete labels (classes).

Examples: Fraud detection, sentiment analysis.
Popular algorithm: Logistic regression, SVM, decision trees.

Example: Logistic regression

from sklearn.linear_model import LogisticRegression

clf = LogisticRegression()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)

🔹 Clustering

Groups data based on similarity without predefined labels.

Examples: Market segmentation, image compression.
Popular algorithm: K-means, DBSCAN.

Example: K-means again (for emphasis)

from sklearn.cluster import KMeans

model = KMeans(n_clusters=3)
model.fit(X)

📚 Further Resources

3. Gradient Descent and Backpropagation

🔹 Gradient Descent

An optimization algorithm used to minimize the loss function by iteratively updating parameters.

Concept:

Example: Manual gradient descent for linear regression

w = 0
b = 0
learning_rate = 0.01

for epoch in range(100):
    y_pred = w * X + b
    loss = ((y - y_pred) ** 2).mean()
    
    dw = -2 * (X * (y - y_pred)).mean()
    db = -2 * (y - y_pred).mean()

    w -= learning_rate * dw
    b -= learning_rate * db

🔹 Backpropagation

The algorithm used in neural networks to compute gradients via the chain rule and update weights layer by layer.

Example: Backprop sketch in a 2-layer NN

# Forward pass
z1 = X @ W1 + b1
a1 = relu(z1)
z2 = a1 @ W2 + b2
a2 = softmax(z2)

# Backward pass
dL_dz2 = a2 - y_true
dL_dW2 = a1.T @ dL_dz2

📚 Further Resources

4. Cross-Validation and Performance Metrics

🔹 Cross-Validation

Technique to assess model generalization by splitting data into training and validation sets multiple times.

Example: K-Fold cross-validation

from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier

scores = cross_val_score(RandomForestClassifier(), X, y, cv=5)
print("Average accuracy:", scores.mean())

🔹 Performance Metrics

Measure how well your model performs.

Example: Classification metrics

from sklearn.metrics import classification_report, confusion_matrix

print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))

Example: Regression metrics

from sklearn.metrics import mean_squared_error, r2_score

print("MSE:", mean_squared_error(y_test, y_pred))
print("R2:", r2_score(y_test, y_pred))

📚 Further Resources

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