LATEST POSTS
Machine learning fundamental validation metrics
What is a validation metric? A validation metric is a formula we use during model validation to confront the model predictions with the actual output values. The choice of a…
Model validation: definition, examples and Python implementation
What is model validation? Model validation is the step that comes after training. During model validation, we evaluate the accuracy of our model by seeing how it performs with data…
Data in machine learning: collection, types and structure
In machine learning, we can identify data as a set of observations or measurements, called dataset, used to train and test a machine learning model. Data are crucial because artificial…
Bias and variance of a model
Bias and variance are 2 fundamental metrics to describe a model’s ability to resolve a problem. Let’s say we have a dataset like this one. We want to represent the…
Code linear regression from scratch in Python
In this article, I’ll show you how to program linear regression from scratch in Python using ONLY MATH. Let’s get started. Why is coding linear regression from scratch useful? When…
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