SciKit-Learn

In this Machine Learning Capstone course, you will be using various Python-based machine learning libraries such as Pandas, scikit-learn, Tensorflow/Keras, to: build a course recommender system; analyze course related datasets, calculate cosine similarity, and create a similarity matrix; create recommendation systems by applying your knowledge of KNN, PCA, and [...]

In the first course of the Machine Learning Specialization, you will: build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn; build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic [...]

This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a [...]

In this course students will explore supervised machine learning techniques using the python scikit learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes. Building on the previous courses in the specialization, students will apply methods such as support vector [...]

This course will teach you how to leverage the power of Python and artificial intelligence to create and test hypothesis. We'll start for the ground up, learning some basic Python for data science before diving into some of its richer applications to test our created hypothesis. We'll learn some [...]

The objective of this course is to introduce Computational Statistics to aspiring or new data scientists. The attendees will start off by learning the basics of probability, Bayesian modeling and inference. This will be the first course in a specialization of three courses .Python and Jupyter notebooks will be [...]

The "Regression Analysis" course equips students with the fundamental concepts of one of the most important supervised learning methods, regression. Participants will explore various regression techniques and learn how to evaluate them effectively. Additionally, students will gain expertise in advanced topics, including polynomial regression, regularization techniques (Ridge, Lasso, and [...]

Este curso se sumerge en los conceptos básicos del aprendizaje automático mediante un lenguaje de programación accesible y conocido, Python. En este curso, repasaremos dos componentes principales. Primero, aprenderá sobre el propósito del aprendizaje automático y dónde se aplica al mundo real.