Python · Machine Learning · Data Science · AI
Learn the basics of Machine Learning in this introductory course.
Livello intermedio
Contiene 8 Progetti, 13 Lezioni e altro
In italiano e inglese
Durata totale 31.6 ore
Con certificato di completamento
Cos'è?
Machine learning, the field of computer science that gives computer systems the ability to learn from data, is one of the hottest topics in computer science.
Machine learning is transforming the world: from spam filtering in social networks to computer vision for self-driving cars, the potential applications of machine learning are vast.
This course covers the foundational machine learning algorithms that will help you advance in your career. Whether you’re trying to analyze a dataset using machine learning, or you’re a data analyst trying to upgrade your skills, this course is the best place to start.
You should be comfortable with Python, including functions, control flow, lists, and loops.
31.6 ore
Intermedio
In italiano
Certificato finale
Sommario
1. Introduction to Machine Learning
What is Machine Learning and how do we use it?
2. Linear Regression
Given a set of points, find a line that fits the data best! Even this simple form of regression allows us to predict future points.
3. Multiple Linear Regression
**Multiple Linear Regression** uses two or more independent variables to predict the value of the dependent variable.
4. Yelp Regression Project
Practice your regression skills on a real-world dataset provided by Yelp!
5. Classification Vs Regression
Learn about the two types of Supervised Learning algorithms, for predicting different kinds of output.
6. Classification: K-Nearest Neighbors
K-Nearest Neighbors is a supervised machine learning algorithm for classification. You will implement and test this algorithm on several datasets.
7. Logistic Regression
Find the probability of data samples belonging to a specific class with one of the most popular classification algorithms.
8. Decision Trees
In this course, you will learn how to build and use decision trees and random forests - two powerful supervised machine learning models.
9. Clustering: K-Means
Clustering is the most well-known unsupervised learning technique. It finds structure in unlabeled data by identifying similar groups.
10. Perceptron
Learn about the most basic type of neural net, the single neuron perceptron! You will use it to divide linearly-separable data.
11. Artificial Intelligence Decision Making: Minimax
In this course, you'll learn how to create a game playing AI that can play Tic Tac Toe and Connect Four.
252 studenti si sono iscritti al percorso nell'ultima settimana. Cosa aspetti?
Progetti
Handwriting Recognition using K-Means
In this project, you will be using K-Means clustering and scikit-learn to cluster images of handw...
Leggi di più
Perceptron Logic Gates
Train Perceptrons to work like logic gates! This simple neuron can act as an AND or an OR gate.
Predicting Income with Random Forests
Use random forests to predict the income of a person based on census data.
Non sarai mai solo. Potrai confrontarti e chiedere aiuto a persone come Marco e tutti gli altri studenti che stanno seguendo questo percorso
metodo devv
Imparare a programmare significa molto più che memorizzare la sintassi. Ti aiutiamo a pensare come un vero programmatore.
Come un consulente di carriera, ti guidiamo in ogni passaggio. Imparerai la cosa giusta al momento giusto, tutto in un unico posto.
Acquisisci esperienza pratica man mano che procedi creando progetti degni di un portfolio che ti aiuteranno a ottenere il tuo prossimo lavoro.
Puoi iniziare a seguire questo percorso gratis e ora. Cosa aspetti?
Scordati le seplici lezioni frontali. All’interno di questo percorso abbiamo unito diverse modalità di apprendimento che ti permetteranno di imparare al meglio.
13 Lezioni
9 Quiz
8 Progetti
7 Articoli
1 Testo
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