Lecture 6 explains clustering in Data Mining, including K-Means, K-Medoids, hierarchical clustering, and DBSCAN with diagrams, algorithms, examples, Python code, real-world applications, and comparisons for BS CS, BS AI, BS IT & Data Science students. Clustering is one of the…
Lecture 5 covers Classification in Data Mining with detailed explanations of Decision Trees, Naive Bayes, KNN, and Logistic Regression. Includes formulas, diagrams, examples, coding, real-world applications, and comparisons. Classification is one of the most widely used techniques in Data Mining…
Python Variables and Data Types in Lecture 2 of the Python for Data Science course. Beginner-friendly explanation, real examples, quiz and practice exercises. What You Will Learn After this lecture, you will be able to: Lecture 1 – Python Installation…
If you’re completely new to programming and Data Science, this is where your journey actually starts. In this first lecture, we’ll set up everything you need: Python, Anaconda, Jupyter Notebook and by the end, you’ll be able to run your…
Intelligent Coding and Code Generation: AI-assisted code completion, program synthesis, and ML-powered static code analysis for smarter software development. Code Completion and Suggestion Tools Modern development environments use AI to assist programmers in writing code faster and more accurately. Key…
Lecture 4 explains Association Rule Mining, including Apriori, FP-Growth, support, confidence, lift, frequent itemsets, market basket analysis, diagrams, formulas, step-by-step examples, and Python implementation perfect for BS CS, BS AI, BS IT, and Data Science students. Association Rule Mining is…