Fundamental Concepts of Machine Learning Deep Learning

What You’ll Learn 📚

• Core concepts of Machine Learning and Deep Learning
• Key terminology and definitions
• Simple to complex examples with code
• Common questions and answers
• Troubleshooting tips for common issues

Introduction to Machine Learning and Deep Learning

Machine Learning is a subset of artificial intelligence that focuses on building systems that can learn from data. Deep Learning is a further subset of ML that uses neural networks with many layers (hence ‘deep’) to analyze various factors of data.

Think of Machine Learning as teaching a computer to recognize patterns, and Deep Learning as teaching it to understand complex patterns in a more human-like way.

Key Terminology

• Algorithm: A set of rules or instructions given to an AI, which it uses to solve problems.
• Model: The output of a machine learning algorithm after it has been trained on data.
• Training: The process of teaching a model by feeding it data.
• Neural Network: A series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data.

Let’s Start with a Simple Example 🐣

Imagine teaching a computer to recognize cats in photos. You’d show it thousands of pictures labeled ‘cat’ or ‘not cat’ and let it learn the patterns that differentiate them.

# Import necessary libraries
import numpy as np
from sklearn.linear_model import LinearRegression

# Sample data
X = np.array([[1], [2], [3], [4], [5]])  # Feature
y = np.array([2, 3, 5, 7, 11])          # Target

# Create a linear regression model
model = LinearRegression()

# Train the model
model.fit(X, y)

# Make a prediction
prediction = model.predict(np.array([[6]]))
print(f'Prediction for input 6: {prediction[0]}')

Progressively Complex Examples 🌱

Example 2: Logistic Regression

# Import necessary libraries
from sklearn.linear_model import LogisticRegression

# Sample data
X = np.array([[0], [1], [2], [3], [4], [5]])  # Feature
y = np.array([0, 0, 0, 1, 1, 1])             # Target (binary)

# Create a logistic regression model
model = LogisticRegression()

# Train the model
model.fit(X, y)

# Make a prediction
prediction = model.predict(np.array([[1.5]]))
print(f'Prediction for input 1.5: {prediction[0]}')

Example 3: Neural Network with Keras

# Import necessary libraries
from keras.models import Sequential
from keras.layers import Dense

# Sample data
X = np.array([[0], [1], [2], [3], [4], [5]])  # Feature
y = np.array([0, 0, 0, 1, 1, 1])             # Target (binary)

# Create a simple neural network
model = Sequential()
model.add(Dense(1, input_dim=1, activation='sigmoid'))

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

# Train the model
model.fit(X, y, epochs=100, verbose=0)

# Make a prediction
prediction = model.predict(np.array([[1.5]]))
print(f'Prediction for input 1.5: {prediction[0][0]}')