Master the fundamentals of artificial intelligence and modern machine learning technologies with hands-on examples and interactive code experiments.
🚀 Start Learning AITrain models using supervised, unsupervised, and reinforcement learning techniques.
# Example: Linear Regression
import numpy as np
from sklearn.linear_model import LinearRegression
X = np.array([[1], [2], [3]])
y = np.array([1, 2, 3])
model = LinearRegression()
model.fit(X, y)
print(model.predict([[4]]))
Build deep learning models using convolutional and recurrent network architectures.
# Example: Simple Neural Network
import tensorflow as tf
model = tf.keras.Sequential([
tf.keras.layers.Dense(1, input_shape=(1,))
])
model.compile(optimizer='sgd', loss='mean_squared_error')
model.fit([[1, 2, 3]], [3, 4, 5], epochs=50)
Process human language using tokenization, embeddings, and transformer architectures.
# Example: Text Tokenizer
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
print(tokenizer.encode('Hello, how are you?'))
Machine learning models analyze medical imaging data to detect diseases and improve diagnosis accuracy.
Neural networks predict market trends, detect fraud patterns, and automate investment strategies.
AI enables self-driving vehicles through computer vision and sensor fusion technologies.
Adaptive learning platforms use AI to create personalized study paths and real-time feedback.
Start building your first AI model with our guided tutorial. Get hands-on practice with real datasets using Python and popular AI frameworks.