LATEST POSTS
Code random forest from scratch in Python
In this post, I’ll show you how to program a random forest from scratch in Python using ONLY MATH. Why is coding a random forest from scratch useful? When studying…
The complete guide to handling missing values
What are missing values in machine learning? Missing values in a dataset indicate the absence of observations. The danger of missing values Why are missing values a problem for our…
The complete guide to encoding categorical features
What are categorical features – recap In categorical features, measurements can assimilate a number of limited and fixed values, called “categories“. There are 2 types of categorical features: Why can’t…
What is feature engineering? Definition, techniques and importance
What is feature engineering? Feature engineering is selecting, extracting, and transforming features from raw data to create a new dataset useful for building predictive models. This new dataset is compatible…
What is a confusion matrix?
Types of classification outputs Positive and negative outputs In a classification problem, there are 2 types of categories, positive and negative. Positive categories are labels with a particular characteristic that…
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