Module - Machine Learning Basics

Master ML Fundamentals

Build a strong foundation in Machine Learning. Learn Linear Regression, Logistic Regression, and essential Model Evaluation techniques to start your ML journey.

3 Core Topics

10+ Concepts Covered

2 ML Algorithms

>>> from sklearn.linear_model import LinearRegression
>>> model.fit(X_train, y_train)
Model trained successfully!

Core Concepts

What You'll Learn

Master the fundamental building blocks of Machine Learning

Linear Regression

Predict continuous values by finding the best-fit line through data points. Understand slope, intercept, and the math behind predictions.

Logistic Regression

Classify data into categories using the sigmoid function. Learn binary classification and probability predictions.

Model Evaluation

Assess model performance using metrics like accuracy, precision, recall, MSE, and R² score. Make data-driven decisions.

ML Basics Curriculum

Learning Path

Master Machine Learning fundamentals with our structured curriculum

Topic 01

Linear Regression

What is Linear Regression?

Simple vs Multiple Regression

Cost Function & Gradient Descent

Training the Model

Making Predictions

Implementation with Sklearn

from sklearn.linear_model import LinearRegression

# Create and train the model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

Topic 02

Logistic Regression

What is Logistic Regression?

Sigmoid Function

Binary Classification

Probability Predictions

Decision Boundary

Implementation with Sklearn

from sklearn.linear_model import LogisticRegression

# Create and train classifier
clf = LogisticRegression()
clf.fit(X_train, y_train)

# Predict probabilities
proba = clf.predict_proba(X_test)

Topic 03

Model Evaluation Basics

Train-Test Split

Accuracy Score

Precision & Recall

Confusion Matrix

MSE & RMSE (Regression)

R² Score

from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.model_selection import train_test_split

# Evaluate model
accuracy = accuracy_score(y_test, y_pred)
cm = confusion_matrix(y_test, y_pred)

Skills You'll Gain

ML Expertise

Master these essential Machine Learning skills

Regression Analysis

Predict continuous values and understand relationships between variables

Classification

Categorize data into classes using logistic regression

Model Evaluation

Assess and compare model performance using key metrics

Sklearn Implementation

Build ML models using scikit-learn library

ML Intuition

Understand when to apply different algorithms

Data Preparation

Split and prepare data for training and testing

Master ML Fundamentals

What You'll Learn

Linear Regression

Logistic Regression

Model Evaluation

Learning Path

Linear Regression

Logistic Regression

Model Evaluation Basics

ML Expertise

Regression Analysis

Classification

Model Evaluation

Sklearn Implementation

ML Intuition

Data Preparation

Ready to Start Your ML Journey?