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Supervised Learning
Classification Methods
➥ Naive Bayes
➥ Logistic Regression
➥ K-Nearest Neighbors (KNN)
➥ Random Forest
➥ Support Vector Machine (SVM)
➥ Decision Trees
Regression Methods
➥ Random Forest
➥ Support Vector Machine
➥ Decision Tree
➥ Simple Linear Regression
➥ Multivariate Regression
➥ Lasso Regression
Unsupervised Learning
Clustering Techniques
➥ k-Means Clustering
➥ Independent Component Analysis
➥ DBSCAN Algorithm
➥ Principal Component Analysis
Association
➥ Frequent Pattern Growth
➥ Apriori Algorithm
Anomaly Detection
➥ Isolation Forest Algorithm
➥ Z-score Algorithm
Reinforcement Learning
Model-free RL
➥ Q-Learning
➥ SARSA (State-Action-Reward-State-Action)
➥ Policy Gradient Methods
➥ Deep Q-Networks (DQN)
Model-based RL
➥ Dyna-Q
➥ Monte Carlo Tree Search (MCTS)
➥ Model Predictive Control (MPC)
➥ Learning with Models
Semi-Supervised Learning
Classification
➥ Self-ensembling
➥ Support Vector Machines
➥ Label Propagation
➥ Co-Training
➥ Gaussian Mixture Models (GMM)
➥ Graph Neural Networks
➥ Pseudo-labeling
➥ Active Learning
➥ Mutual Information Maximization
Regression
➥ Linear Regression
➥ Polynomial Regression
➥ Ridge Regression
➥ Lasso Regression
➥ Support Vector Regression
You Should Know:
Practical Implementation of ML Algorithms
1. Naive Bayes in Python
from sklearn.naive_bayes import GaussianNB
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
Load dataset
data = load_iris()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3)
Train model
model = GaussianNB()
model.fit(X_train, y_train)
Predict
predictions = model.predict(X_test)
print("Accuracy:", model.score(X_test, y_test))
2. k-Means Clustering in R
library(stats) data <- iris[, 1:4] kmeans_model <- kmeans(data, centers=3) print(kmeans_model$cluster)
3. Q-Learning with OpenAI Gym
import gym
import numpy as np
env = gym.make('FrozenLake-v1')
Q = np.zeros((env.observation_space.n, env.action_space.n))
Training loop
for episode in range(1000):
state = env.reset()
done = False
while not done:
action = np.argmax(Q[state, :])
next_state, reward, done, _ = env.step(action)
Q[state, action] = reward + 0.9 np.max(Q[next_state, :])
state = next_state
4. Linux Command for Data Processing
Extract features from CSV
awk -F ',' '{print $1, $2}' dataset.csv > extracted_features.txt
5. Windows PowerShell for Model Training
python -m pip install scikit-learn pandas python train_model.py --dataset data.csv --model random_forest
What Undercode Say:
Machine learning algorithms form the backbone of AI-driven solutions. Understanding their implementation in Python, R, and reinforcement learning environments like OpenAI Gym is essential. Linux commands (awk, grep) aid in preprocessing, while Windows PowerShell automates model training.
Expected Output:
A well-structured guide to ML algorithms with executable code snippets for hands-on learning.
Note: WhatsApp and Telegram URLs have been removed as per instructions.
References:
Reported By: Habib Shaikh – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
