Introduction to Machine Learning | Machine Learning Guide | Ephizen

What is Machine Learning?#

Machine Learning is the science of getting computers to learn from data instead of being explicitly programmed.

Simple Definition

Traditional programming: Rules + Data = Answers Machine Learning: Data + Answers = Rules

Why Machine Learning Matters#

Some problems are too complex for explicit rules:

Tasks Where ML Excels

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Types of Machine Learning#

Feature	Type	Description	Example
Supervised	Learn from labeled data	Input → Output mapping	Spam detection
Unsupervised	Find patterns in unlabeled data	Discover structure	Customer segmentation
Reinforcement	Learn from trial and error	Maximize rewards	Game playing AI

The ML Pipeline#

Step 1

Problem Definition

Define what you're trying to predict or discover

Step 2

Data Collection

Gather relevant, high-quality data

Step 3

Data Preparation

Clean, transform, and split the data

Step 4

Model Selection

Choose appropriate algorithms

Step 5

Training

Let the model learn from data

Step 6

Evaluation

Test on unseen data, measure performance

Step 7

Deployment

Put the model into production

Key Concepts#

Features and Labels#

python

# Features (X): Input variables the model uses to make predictions
features = ["age", "income", "credit_score", "employment_years"]

# Labels (y): What we're trying to predict
labels = ["approved", "rejected"]  # For loan approval

# Example data
X = [[25, 50000, 720, 3],
     [45, 80000, 680, 15],
     [30, 60000, 750, 5]]

y = ["approved", "rejected", "approved"]

Training vs Testing#

python

from sklearn.model_selection import train_test_split

# Split data: 80% for training, 20% for testing
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Train on training data
model.fit(X_train, y_train)

# Evaluate on test data (never seen during training)
accuracy = model.score(X_test, y_test)

Critical Rule

Never evaluate on training data. Your model might just memorize, not learn.

Common Algorithms#

📈

Linear Regression

Predicts continuous values. Simple and interpretable.

🌳

Decision Trees

Makes decisions based on feature thresholds. Easy to visualize.

🌲

Random Forest

Ensemble of trees. More accurate, less prone to overfitting.

🧠

Neural Networks

Learns complex patterns. Powers deep learning.

Your First ML Model#

python

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Load data
iris = load_iris()
X, y = iris.data, iris.target

# Split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Train
model = DecisionTreeClassifier()
model.fit(X_train, y_train)

# Predict
predictions = model.predict(X_test)

# Evaluate
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy:.2%}")  # ~97%

When to Use ML#

Pros

Pattern recognition at scale
Handling complex, non-linear relationships
Improving with more data
Automating decision-making

Cons

Requires quality data
Can be a black box
May perpetuate biases
Computationally expensive

Key Takeaways#

Remember

Machine learning is powerful, but it's not magic. Good data, clear problem definition, and proper evaluation are essential. Start simple, measure everything, and iterate.

Feature

Type

Description

Example

Supervised

Learn from labeled data

Input → Output mapping

Spam detection

Unsupervised

Find patterns in unlabeled data

Discover structure

Customer segmentation

Reinforcement

Learn from trial and error

Maximize rewards

Game playing AI

The ML Pipeline#

Step 1

Problem Definition

Define what you're trying to predict or discover

Step 2

Data Collection

Gather relevant, high-quality data

Step 3

Data Preparation

Clean, transform, and split the data

Step 4

Model Selection

Choose appropriate algorithms

Step 5

Training

Let the model learn from data

Step 6

Evaluation

Test on unseen data, measure performance

Step 7

Deployment

Put the model into production

Key Concepts#

Features and Labels#

python

# Features (X): Input variables the model uses to make predictions
features = ["age", "income", "credit_score", "employment_years"]

# Labels (y): What we're trying to predict
labels = ["approved", "rejected"]  # For loan approval

# Example data
X = [[25, 50000, 720, 3],
     [45, 80000, 680, 15],
     [30, 60000, 750, 5]]

y = ["approved", "rejected", "approved"]

Training vs Testing#

python

from sklearn.model_selection import train_test_split

# Split data: 80% for training, 20% for testing
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Train on training data
model.fit(X_train, y_train)

# Evaluate on test data (never seen during training)
accuracy = model.score(X_test, y_test)

Critical Rule

Never evaluate on training data. Your model might just memorize, not learn.

Your First ML Model#

python

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Load data
iris = load_iris()
X, y = iris.data, iris.target

# Split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Train
model = DecisionTreeClassifier()
model.fit(X_train, y_train)

# Predict
predictions = model.predict(X_test)

# Evaluate
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy:.2%}")  # ~97%

What is Machine Learning?#

Why Machine Learning Matters#

Tasks Where ML Excels

Types of Machine Learning#

The ML Pipeline#

Problem Definition

Data Collection

Data Preparation

Model Selection

Training

Evaluation

Deployment

Key Concepts#

Features and Labels#

Training vs Testing#

Common Algorithms#

Linear Regression

Decision Trees

Random Forest

Neural Networks

Your First ML Model#

When to Use ML#

Key Takeaways#

Ready to level up your skills?

What is Machine Learning?#

Why Machine Learning Matters#

Tasks Where ML Excels

Types of Machine Learning#

The ML Pipeline#

Problem Definition

Data Collection

Data Preparation

Model Selection

Training

Evaluation

Deployment

Key Concepts#

Features and Labels#

Training vs Testing#

Common Algorithms#

Linear Regression

Decision Trees

Random Forest

Neural Networks

Your First ML Model#

When to Use ML#

Key Takeaways#

Ready to level up your skills?