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O(1) - Constant Time: Simple tasks that take the same amount of time no matter how much data you have, like finding an item in a list by its position.

O(log n) - Logarithmic Time: Tasks that take less time as the data grows, like finding an item in a sorted list by repeatedly dividing it in half.

O(n) - Linear Time: Tasks that take more time as the data grows, like counting all items in a list by checking each one.

O(n log n) - Linearithmic Time: Tasks that get a bit slower as the data grows, like sorting a list using efficient methods such as merge sort or quick sort.

O(n²) - Quadratic Time: Tasks that get noticeably slower as the data grows, like sorting a list using simpler methods like bubble sort or finding all pairs in a list.

O(2^n) - Exponential Time: Tasks that get much slower as the data grows, like finding all subsets of a set or solving complex problems like the traveling salesman using a basic approach.

O(n!) - Factorial Time: Tasks that get extremely slow as the data grows, like solving problems that involve checking every possible arrangement of items.

Expert Python Programming (2021)

100 likes = new books

Here's a step-by-step beginner's roadmap for learning machine learning:🪜📚

Learn Python: Start by learning Python, as it's the most popular language for machine learning. There are many resources available online, including tutorials, courses, and books.

Understand Basic Math: Familiarize yourself with basic mathematics concepts like algebra, calculus, and probability. This will form the foundation for understanding machine learning algorithms.

Learn NumPy, Pandas, and Matplotlib: These are essential libraries for data manipulation, analysis, and visualization in Python. Get comfortable with them as they are widely used in machine learning projects.

Study Linear Algebra and Statistics: Dive deeper into linear algebra and statistics, as they are fundamental to understanding many machine learning algorithms.

Introduction to Machine Learning: Start with courses or tutorials that introduce you to machine learning concepts such as supervised learning, unsupervised learning, and reinforcement learning.

Explore Scikit-learn: Scikit-learn is a powerful Python library for machine learning. Learn how to use its various algorithms for tasks like classification, regression, and clustering.

Hands-on Projects: Start working on small machine learning projects to apply what you've learned. Kaggle competitions and datasets are great resources for this.

Deep Learning Basics: Dive into deep learning concepts and frameworks like TensorFlow or PyTorch. Understand neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

Advanced Topics: Explore advanced machine learning topics such as ensemble methods, dimensionality reduction, and generative adversarial networks (GANs).

Stay Updated: Machine learning is a rapidly evolving field, so it's important to stay updated with the latest research papers, blogs, and conferences.

🧠👀Remember, the key to mastering machine learning is consistent practice and experimentation. Start with simple projects and gradually tackle more complex ones as you gain confidence and expertise. Good luck on your learning journey!

Operating Systems
Pranabananda Chakraborty, 2023

Advanced Concepts in Operating Systems
Mukesh Singhal, 2008 (scanned)