Tutorials Categories based on Courses
Welcome to learnbettercomputing comprehensive tutorial library on Machine Learning and Algorithm Analysis. Courses are organized into multiple categories so that you can quickly navigate to the topics that interest you most. Each category is clickable—simply click on a category name to reveal and access a list of tutorials dedicated to that subject.
For Machine Learning, it offer categories such as Data Structures (Introduction to Data Structures), Dimensionality Reduction (Techniques for reducing the number of features), Classification (Supervised learning for categorizing data), Regression (Predicting continuous values based on input features), Neural Networks (Deep learning models inspired by the human brain), Model Evaluations (Evaluating the performance of machine learning models), and Deep Learning (Advanced machine learning with multi-layer neural networks). Click any of these categories to view the tutorials that provide both foundational insights and advanced strategies in machine learning.
Similarly, the Algorithm Analysis section is divided into clear, focused categories. Explore Design and Analysis of Algorithms with subtopics including Asymptotic Notations (Understanding Big O, Big Omega, and Big Theta notation), Time Complexities (Analyzing the time efficiency of algorithms and their execution time in different cases), Space Complexities (Understanding the space requirements of algorithms and their memory usage), Sorting (Exploring different sorting algorithms like QuickSort, MergeSort, and BubbleSort), and Searching (Investigating searching algorithms like Binary Search and Linear Search). Each category is designed to provide practical tutorials and examples that enhance your understanding of algorithm performance and design. Enjoy exploring these categories and deepening your technical knowledge!