LATEST POSTS
How to build a machine learning model in 6 steps
This article explains the steps to build a machine learning model, including explanations, examples, and Python code that everyone can try to execute and modify as they like. In this…
Random forest: structure, training and Python code
What is a random forest? A random forest is a bagging machine-learning model that combines the output of numerous decision trees to make predictions. Random forest training As said before,…
Decision tree: structure, training and Python code
What a decision tree is A decision tree is a non-parametric supervised machine learning model that predicts outputs by extracting various conditions from the data that an input may or…
What is an algorithm? Definition, structure and examples
An algorithm is a detailed step-by-step set of instructions aimed at solving a problem. Algorithms are the beating heart of modern computing. Every time you perform a search on the…
What is machine learning? Definition and training approaches
What machine learning is (definition) Machine learning is a subfield of artificial intelligence that studies the development of machines able to find meaningful pattern and relationships in a large input…
WHAT IS MACHINE LEARNING ?
Machine learning (ML) is a subfield of artificial intelligence that studies the development of algorithms that can learn patterns from data and make predictions based on them. These "human-like intelligent" systems are called models.
Machine learning it's a broad field that also includes processing input data, evaluating model accuracy and managing AI influence on society, ensuring these products aren't affected by gender, ethnic, and cultural biases.
In today's world, machine learning solutions are becoming increasingly important. They are applied in various sectors like natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine.
ABOUT THIS BLOG
Inside Algorithms is a constantly updated blog with articles regarding machine learning, which studies the development of artificial intelligence algorithms.
Thanks to Inside Algorithms you will learn how artificial intelligence like ChatGPT works in the core and how to apply your knowledge to real-world scenarios. Cool isn’t it?
ABOUT THIS BLOG
Inside Algorithms is a constantly updated blog with articles regarding machine learning, which studies the development of artificial intelligence algorithms.
Thanks to Inside Algorithms you will learn how artificial intelligence like ChatGPT works in the core and how to apply your knowledge to real-world scenarios. Cool isn’t it?
POST TOPICS
Fundamentals
Fundamentals concepts of machine learning
Feature engineering
Data management methods to provide more meaningful input data
ML Algorithms
Algorithms that can learn relationships between data to make predicitons
Validation
Different techniques for measuring the accuracy of a model
Real-life projects
Program artificial intelligence models with real-life data and goals
WHY FOLLOW THIS BLOG?
Clear explanations
I struggle with math, so in order to understand the concepts I have to make an effort to find simple and clear explanations. These solutions are the ones I share with you.
Algorithms from scratch
Another thing that helps me fully understand machine learning algorithms is to rewrite them from 0. I forget about all the convenient modern Python libraries and just use math.
Real-life projects
Through examples and projects taken from real situations, with real datasets and true queries, you will be able to better learn this discipline and develop a practical view of theoretical concepts.
Machine intelligence is the last invention that humanity will ever need to make.
– Nick Bostrom, philosofer and AI expert