Geeky Codes | AI Engineering, Data Engineering & Machine Learning Tutorials
Introduction: String manipulation is a fundamental aspect of programming, and understanding efficient techniques for performing string operations is essential. In this tutorial, we’ll explore an efficient approach to reverse substrings within a string, leveraging Python programming and a thorough understanding of string manipulation. Understanding the Problem: Given a string ‘S’ and a set of ‘M’ … Read more
Introduction: In the realm of machine learning, ensemble learning techniques such as AdaBoost and Gradient Boosting have revolutionized the way we approach classification and regression tasks. These powerful algorithms harness the collective intelligence of multiple weak learners to create a robust and accurate predictive model. In this tutorial, we’ll embark on a journey to explore … Read more
Given a number N, find its square root. You need to find and print only the integral part of square root of N. For eg. if number given is 18, answer is 4. Input format : Output Format : Constraints : Understanding the Problem: Given a non-negative integer ‘N’, our objective is to find and … Read more
Introduction: In Python programming, clear and concise documentation is essential for understanding code functionality, usage, and purpose. Docstrings, Python’s built-in documentation feature, serve as invaluable tools for documenting modules, classes, functions, and methods. In this tutorial, we’ll delve into the world of docstrings, exploring their types, usage, and best practices. What are Docstrings? Docstrings are … Read more
Introduction: In the world of string manipulation, unraveling patterns and matching sequences play a pivotal role in various applications. In this tutorial, we’ll embark on a journey to decode word patterns, where we’ll determine if two strings follow the same pattern. Through the lens of Java programming, we’ll explore an efficient approach to tackle this … Read more
Introduction: In binary trees, exploring their structure and nodes in a systematic manner is crucial for various operations and analyses. One such traversal method is the level order traversal, which traverses the tree level by level, starting from the root node. Through this tutorial, we’ll delve into the intricacies of level order traversal and learn … Read more
Problem Statement: Given a positive integer ‘N’, our objective is to compute and return the minimum number of steps needed to reduce ‘N’ to 1. We have three permissible operations: Understanding the Approach: To efficiently solve this problem, we’ll employ dynamic programming to compute the minimum steps needed for each integer from 1 to ‘N’. … Read more
Introduction: In the realm of algorithmic problem-solving, the quest for the maximum sum of any contiguous subarray within a given array is a classic challenge. In this tutorial, we’ll embark on a journey to conquer this challenge using Kadane’s algorithm, a powerful tool that operates with a time complexity of O(N). Problem Statement: Given an … Read more
Introduction: Dynamic Programming (DP) is a powerful algorithmic technique used to solve a variety of optimization problems. One classic problem where DP shines is in finding the longest palindromic subsequence in a given string. In this tutorial, we’ll delve into the intricacies of this problem and learn how to tackle it using DP. Problem Statement: … Read more
You are given a string ‘S’. Your task is to check whether the string is palindrome or not. For checking palindrome, consider alphabets and numbers only and ignore the symbols and whitespaces. Note : Example : Input format : Output format : Note : Follow Up : Constraints : Implementation in Python
For Linear Regression in Machine learning with two variables we have to find 2 coefficient. In case of overfitting these 2 coefficients can be very high. Y=mX+c So to handle this, Idea is that we want to reduce the value of coefficient(m). By doing this biasness can increase but variance decreases. Which is called bias … Read more
Introduction I decided to write this kernel because Titanic: Machine Learning from Disaster is one of my favorite competitions on Kaggle. This is a beginner level kernel which focuses on Exploratory Data Analysis and Feature Engineering. A lot of people start Kaggle with this competition and they get lost in extremely long tutorial kernels. This is a short kernel compared … Read more
Introduction Feature engineering, often hailed as the cornerstone of machine learning, holds the power to transform raw data into actionable insights. In the realm of predictive modeling, where the quality of features can significantly influence model performance, mastering the art of feature engineering is indispensable. In this comprehensive guide, we’ll embark on a Titanic journey … Read more
With thousands of parameters you can fit the whole zoo. Deep neural networks typically have tens of thousands of parameters, sometimes even millions. With so many parameters, the network has an incredible amount of freedom and can fit a huge variety of complex datasets. But this great flexibility also means that it is prone to … Read more
Introduction In the dynamic landscape of machine learning, Multilayer Perceptrons (MLPs) emerge as formidable tools capable of handling both regression and classification tasks with finesse. Whether you’re predicting housing prices or sorting emails, understanding how to tailor MLP architectures and activations is pivotal for optimizing performance. Regression MLPs Crafting an MLP architecture for regression tasks … Read more
An MLP is composed of one (passthrough) input layer, one or more layers of TLUs, called hidden layers, and one final layer of TLUs called the output layer (seeFigure below). The layers close to the input layer are usually called the lower layers, and the ones close to the outputs are usually called the upper … Read more
A jail has a number of prisoners and a number of treats to pass out to them. Their jailer decides the fairest way to divide the treats is to seat the prisoners around a circular table in sequentially numbered chairs. A chair number will be drawn from a hat. Beginning with the prisoner in that … Read more
Introduction: The task at hand is to implement a Python function that finds the kth lexicographically smallest integer in the range from 1 to n. To achieve this, we will use a depth-first search (DFS) approach, exploring the numbers in lexicographical order. Let’s dive into the implementation. Also checkout: Implementation: Explanation: Example: For n = … Read more
SELECT TOP number|percent column_name(s) FROM table_name SQL SELECT TOP Equivalent in MySQL and Oracle: MySQL Syntax: SELECT column_name(s) FROM table_name LIMIT number Example: SELECT * FROM Persons LIMIT 5 Oracle Syntax SELECT column_name(s) FROM table_name WHERE ROWNUM <= number Example SELECT * FROM Persons WHERE ROWNUM <=5 SQL TOP Example The “Persons” table: Now we … Read more
DELETE FROM table_name WHERE some_column=some_value Note: Notice the WHERE clause in the DELETE syntax. The WHERE clause specifies which record or records that should be deleted. If you omit the WHERE clause, all records will be deleted! SQL DELETE Example The “Persons” table: Now we want to delete the person “Tjessem, Jakob” in the “Persons” … Read more