What is an Algorithm?
In this tutorial, we will learn what algorithms are with the help of examples.
An algorithm is a set of well-defined instructions in sequence to solve a problem.
Qualities of a good algorithm
- Input and output should be defined precisely.
- Each step in the algorithm should be clear and unambiguous.
- Algorithms should be most effective among many different ways to solve a problem.
- An algorithm shouldn’t include computer code. Instead, the algorithm should be written in such a way that it can be used in different programming languages.
Examples Of Algorithms In Programming
Algorithm to add two numbers entered by the user
Step 1: Start Step 2: Declare variables num1, num2 and sum. Step 3: Read values num1 and num2. Step 4: Add num1 and num2 and assign the result to sum. sum←num1+num2 Step 5: Display sum Step 6: Stop
Find the largest number among three different numbers
Step 1: Start Step 2: Declare variables a,b and c. Step 3: Read variables a,b and c. Step 4: If a > b If a > c Display a is the largest number. Else Display c is the largest number. Else If b > c Display b is the largest number. Else Display c is the greatest number. Step 5: Stop
Roots of a quadratic equation ax2 + bx + c = 0
Step 1: Start Step 2: Declare variables a, b, c, D, x1, x2, rp and ip; Step 3: Calculate discriminant D ← b2-4ac Step 4: If D ≥ 0 r1 ← (-b+√D)/2a r2 ← (-b-√D)/2a Display r1 and r2 as roots. Else Calculate real part and imaginary part rp ← -b/2a ip ← √(-D)/2a Display rp+j(ip) and rp-j(ip) as roots Step 5: Stop
Factorial of a number entered by the user.
Step 1: Start Step 2: Declare variables n, factorial and i. Step 3: Initialize variables factorial ← 1 i ← 1 Step 4: Read value of n Step 5: Repeat the steps until i = n 5.1: factorial ← factorial*i 5.2: i ← i+1 Step 6: Display factorial Step 7: Stop
Check whether a number is a prime number or not
Step 1: Start Step 2: Declare variables n, i, flag. Step 3: Initialize variables flag ← 1 i ← 2 Step 4: Read n from the user. Step 5: Repeat the steps until i=(n/2) 5.1 If remainder of n÷i equals 0 flag ← 0 Go to step 6 5.2 i ← i+1 Step 6: If flag = 0 Display n is not prime else Display n is prime Step 7: Stop
Find the Fibonacci series till term ≤ 1000.
Step 1: Start Step 2: Declare variables first_term,second_term and temp. Step 3: Initialize variables first_term ← 0 second_term ← 1 Step 4: Display first_term and second_term Step 5: Repeat the steps until second_term ≤ 1000 5.1: temp ← second_term 5.2: second_term ← second_term + first_term 5.3: first_term ← temp 5.4: Display second_term Step 6: Stop
Python Example for Beginners
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes
Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!
Latest end-to-end Learn by Coding Recipes in Project-Based Learning:
Applied Statistics with R for Beginners and Business Professionals
Data Science and Machine Learning Projects in Python: Tabular Data Analytics
Data Science and Machine Learning Projects in R: Tabular Data Analytics
Python Machine Learning & Data Science Recipes: Learn by Coding
R Machine Learning & Data Science Recipes: Learn by Coding
Comparing Different Machine Learning Algorithms in Python for Classification (FREE)
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.
Google –> SETScholars