Welcome to TensorFlow Lesson 1! In this hands-on tutorial, we’ll guide you through building your first AI model using TensorFlow and Python. Whether you’re a beginner or just curious about AI, this lesson will help you understand how machine learning models learn patterns from data.

🌟 What You’ll Learn in This Video:
✅ Installing TensorFlow
✅ Understanding AI & machine learning basics
✅ Creating training data with NumPy
✅ Building a simple neural network
✅ Training the AI model to recognize patterns
✅ Making predictions with the trained model

Code Used in This Lesson:

python

!pip install tensorflow
import tensorflow as tf
import numpy as np

X = np.array([1, 2, 3, 4], dtype=float)
Y = np.array([2, 4, 6, 8], dtype=float)

model = tf.keras.Sequential([
tf.keras.layers.Dense(units=1, input_shape=[1])
])

model.compile(optimizer=’sgd’, loss=’mean_squared_error’)
model.fit(X, Y, epochs=500, verbose=0)

prediction_input = np.array([5.0])
print(model.predict(prediction_input))
—-

🎯 This video is perfect for:
✔️ Beginners in AI & Machine Learning
✔️ Python learners
✔️ Anyone curious about how AI models work

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