Machine Learning (ML) has become one of the most in-demand skills of 2025. From powering search engines to enabling self-driving cars, ML is transforming industries worldwide. However, for beginners, starting the journey can often feel overwhelming due to the vast amount of concepts, tools, and resources available.
This 20-day step-by-step roadmap is designed to provide clarity and direction. It covers essential concepts, practical resources, and practice questions to help beginners progress from foundational knowledge to completing their first machine learning project.
Day 1–2: Introduction to Machine Learning
Goals: Understand the fundamentals of ML, Types of ML, and real-world applications.
Topics: Supervised Learning, Unsupervised Learning, Reinforcement Learning.
Resources:
- Andrew Ng’s Machine Learning Course (Coursera)
- Introduction to Machine Learning – Towards Data Science
Practice Questions:
- What is the difference between supervised and unsupervised learning?
- List three real-world applications of ML.
Day 3–4: Python for Machine Learning
Goals: Gain familiarity with Python, the most widely used programming language for ML.
Topics: NumPy, Pandas, Matplotlib.
Resources:
Practice Questions:
- How do you create a DataFrame in Pandas?
- Plot a simple line graph using Matplotlib.
Day 5–6: Data Preprocessing In Machine Learning
Goals: Learn how to clean and prepare datasets for modeling.
Topics: Handling missing values, feature scaling, encoding categorical data.
Resources:
Practice Questions:
- Why is feature scaling important?
- How do you handle missing data in Pandas?
Day 7–8: Exploratory Data Analysis (EDA)
Goals: Understand how to analyze and interpret datasets.
Topics: Data visualization, correlation analysis, outlier detection.
Resources:
Practice Questions:
- What is the purpose of correlation analysis?
- How can boxplots help detect outliers?
Day 9–10: Regression Models In Machine Learning
Goals: Learn regression techniques for predicting continuous outcomes.
Topics: Simple Linear Regression, Multiple Regression.
Resources:
Practice Questions:
- What is the equation of a linear regression model?
- How do you evaluate regression performance?
Day 11–12: Classification Models In Machine Learning
Goals: Study classification algorithms for categorical predictions.
Topics: Logistic Regression, Decision Trees, Random Forest.
Resources:
- Logistic Regression Explained
- Scikit-learn Decision Trees
Practice Questions:
- How is logistic regression different from linear regression?
- What are the pros and cons of decision trees?
Day 13–14: Model Evaluation In Machine Learning
Goals: Understand metrics used to evaluate ML models.
Topics: Accuracy, Precision, Recall, F1-score, ROC Curve.
Resources:
- Evaluation Metrics for Classification
- Scikit-learn Metrics
Practice Questions:
- Why is accuracy not always the best metric?
- Explain precision vs. recall with an example.
Day 15–16: Feature Engineering
Goals: Improve model performance through effective feature design.
Topics: Feature selection, dimensionality reduction (PCA).
Resources:
- Feature Engineering Techniques
- PCA with Scikit-learn
Practice Questions:
- What is the purpose of PCA?
- How can feature selection reduce overfitting?
Day 17–18: Hyperparameter Tuning
Goals: Optimize models for better results.
Topics: Grid Search, Random Search, Bayesian Optimization.
Resources:
- Hyperparameter Tuning Random Forest (Towards Data Science)
- Bayesian Optimization Explained
Practice Questions:
- Compare grid search and random search methods.
- How does Bayesian optimization improve the search process?
Day 19: Neural Networks Introduction
Goals: Learn the basics of neural networks.
Topics: Perceptron, Activation Functions, Forward Propagation.
Resources:
- Neural Networks from Scratch
- Deep Learning Specialization (Coursera)
Practice Questions:
- What is a perceptron?
- Why are activation functions needed?
Day 20: Final Project Of Roadmap
Goals: Apply all learned concepts in a practical project.
Task: Choose a dataset (Kaggle or UCI Repository), build ML models, and evaluate their performance.
Practice Question:
- What was the best model you built and why?
Machine Learning Hand Written Notes PDF.