Learn the basics and become comfortable using pandas for manipulating machine learning datasets.

Pandas for Machine Learning

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

Numpy Tutorial Exercises

Deploy Models using Streamlit and Render.com

Deploy AI Models Using Flask and Render.com

How to Deal With Outliers

How to Deal With Missing Values

Feature Engineering

Feature Encoding for Categorical Variables

Cookicutter

Calculus

Python for DataScience

Prepare a real dataset to later train a machine learning model

Real Estate Data Cleanup

Introduction to Pandas

Introduction to Numpy

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

K-means Project Tutorial

Use KNN algorithm to build a simple movie recommender system.

K-nearest neighbors Project Tutorial

Introduction to Machine Learning

Cloud Computing

Linear Algebra

Machine Learning Basics

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

ML web app using Flask tutorial

Deploy model using Streamlit and Heroku

Exploring Neural Networks

Intro to deep learning

Exploring Time Series

Time Series

K-nearest neighbors

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

NLP Project Tutorial

Exploring Natural Language Processing

Support Vector Machine

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

Naive Bayes Project Tutorial

Random Forest

Exploring Decision Trees

Regularized Linear Models

Logistic Regression

Model Evaluation

How to take the Exploratory Data-analysis Module

Algorithms and Data Structures Optimization

Standard Normal Table

Plotting Binomial Distribution with Python

Bernoulli Probability with Python

Random Variables

Hypothesis Testing

Boosting Algorithms

Intro to Matplotlib

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

Algorithm Optimization Project Machine Learning

Use your numpy, pandas and matplotib skills to practice a little about descriptive statistics

Descriptive Statistics Exercises Project with Python

Use XGBoost algorithm to optimize your previous models done in Titanic dataset

Boosting Algorithms Project Tutorial

Use a dataset with a lot of variables to practice your regularized linear regression skills and select only some of the features for your model

Regularized Linear Regression Project Tutorial

Use Random Forest algorithm to optimize your previous models done in Titanic dataset

Random Forest Project Tutorial

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

Time Series Project Tutorial

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

Alternative time series project

Use Streamlit to create a machine learning web application.

ML web app using Streamlit 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.

Linear Regression Project Tutorial

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

Decision Tree Project Tutorial

Use a deep learning algorithm to build an image classifier.

Image Classifier Project Tutorial

These are your machine learning final project instructions

Machine Learning Final Project

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.

Machine learning Interactive tutorial exercises

Pandas for Machine Learning

Numpy Tutorial Exercises