Learn Machine learning

How to Train an AI model: Beginners Guide

🤖 Complete Guide to Training an AI in 5 Steps

Deploy model using Streamlit and Heroku

How to Deal With Outliers

Feature Encoding for Categorical Variables

Python for DataScience

Introduction to Machine Learning

Machine Learning Basics

Exploring Time Series

Exploring Natural Language Processing

Exploring Decision Trees

Model Evaluation

Bernoulli Probability with Python

Hypothesis Testing

Intro to Matplotlib

Plotting Distributions with Python

Plotting Functions With Python

Decision Trees

Natural Language Processing

Naive Bayes Algorithm

Discover Naive Bayes: a simple yet powerful classification algorithm based on Bayes’ theorem. Learn its implementations and hyperparameters.

K-nearest neighbors Project Tutorial

Use KNN algorithm to build a simple movie recommender system.

NLP Project Tutorial

Create a url spam detector using NLP techniques and Support Vector Machine algorithm

Algorithm Optimization Project for Machine Learning

Learn the basics on how to optimize or better build algorithms in Python for Machine Learning

Web Scraping Tesla revenues with Python and Beautiful Soup

Use python and the beautiful soup library to download, scrape, clean and visualize the revenue information from Tesla

Time Series Project Tutorial

Use your new skills to help American Express improve their default prediction model.

Linear Regression Project Tutorial

How much will your insurance cost based on a set of variables? Use this small dataset to model the relationship between several explanatory variables and a numerical target using a linear regression algorithm.

Machine Learning Final Project

These are your machine learning final project instructions

Pandas tutorial exercises

Master Pandas, the most popular Python library for machine learning, with our pandas tutorial exercises. Learn to create DataFrames, clean datasets, and more, with exercises developed by experts.

Pandas Exercises And Solutions

Deploy Models using Streamlit and Render.com

How to Deal With Missing Values

Cookicutter

Introduction to Python Pandas

Cloud Computing

Exploring Neural Networks

Time Series

Support Vector Machine

Regularized Linear Models

How to take the Exploratory Data-analysis Module

Plotting Binomial Distribution with Python

Random Variables

Probability

Exploring Linear Regression

Binomial Probability with Python

What is a Linear Regression and how to use it in machine learning models

Discover the power of linear regression, a model that predicts outcomes based on one or more variables. Learn about its five key assumptions and how it can help you understand relationships between data points. Dive into simple and multiple linear regression to enhance your data analysis skills.

Deploy model using Flask and Heroku

Real Estate Data Cleanup

Prepare a real dataset to later train a machine learning model

Hypothesis Testing Exercises in Python

Use your NumPy, Pandas and Matplotlib skills to practice a little about hypothesis testing with Annova and others

Naive Bayes Project Tutorial

Use Naive Bayes algorithm to classify apps reviews as good or bad.

Descriptive Statistics Exercises Project with Python

Use your NumPy, Pandas, and Matplotlib skills to practice a little about descriptive statistics

Build a linear regression model using pandas and python

Learn how to build a linear regression model from scratch: understand a new dataset, perform exploratory data analysis (EDA), model the data, and optimize your model. Discover how the $R^2$ evolves by adjusting the Lasso model's hyperparameter up to a value of 20.

Alternative time series project

Use ARIMA model o create a cpu usage anomaly detection model.

Decision Tree Project Tutorial

Use decision tree algorithm to diagnose diabetes by using patiente medical information from previous medical exams

Logistic Regression Project Tutorial

Logistic Regression is the ideal first algorithm to learn how to model. Without doing much cleaning, train your model and optimize until you are satified with your evaluation and results.

Numpy Tutorial Exercises

This interactive tutorial will help you become familiar on it, master the most used functionalities and help you clean up your first datasets

Tips and tools to build a Data Science and Machine Learning project

When building a Data Science and Machine Learning project there are things to be considered, specially if you are looking for a job. Here you find tips to make a project make a cv stand out from the crowd, and a list of sites where you can find useful datasets to kickstart your project.

Deploy AI Models Using Flask and Render.com

Feature Engineering

Calculus

Introduction to Numpy

Linear Algebra

Intro to deep learning

K-nearest neighbors

Random Forest

Logistic Regression

Standard Normal Table

Algorithms and Data Structures Optimization

Boosting Algorithms

Exploratory Data Analysis and Cleaning with Titanic

Exploring Naive Bayes

Model Hyperparameters Optimization

Unsupervised learning

Intro to AWS SageMaker

K-means Project Tutorial

Use k-means algorithm to segment houses based on their coordinates.

ML web app using Flask tutorial

Use Flask to create a machine learning web application to predict Titanic survival.

Probability Exercises Project in Python

Usa tus habilidades con numpy, pandas y matplotlib para practicar un poco sobre probabilidades.

Boosting Algorithms Project Tutorial

Use XGBoost algorithm

Random Forest Project Tutorial

Use Random Forest algorithm to predict a marketing campaign success by predicting the campaign impressions

ML web app using Streamlit tutorial

Use Streamlit to create a machine learning web application.

Image Classifier Project Tutorial

Use a deep learning algorithm to build an image classifier.

Connecting to a SQL Database Project Tutorial

Create a SQL database using the PostgreSQL engine and using a professional python project structure that includes PIP and environment variables.

How to install python pandas?

Learn how to install Pandas in Python easily on Windows, MacOS, and Ubuntu. Follow our step-by-step guide to correctly set up Pandas for data analysis.