Machine Learning for Everyone

Build the foundation you need to think statistically and to speak the language of your data.

3 hours

4 hours

In this course you will learn the details of linear classifiers like logistic regression and SVM.

4 hours

5 hours

Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

4 hours

In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.

4 hours

Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

4 hours

Learn how to clean and prepare your data for machine learning!

4 hours

4 hours

Create new features to improve the performance of your Machine Learning models.

4 hours

Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.

4 hours

4 hours

Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

4 hours

Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.

4 hours

4 hours

Learn to start developing deep learning models with Keras.

4 hours

Build multiple-input and multiple-output deep learning models using Keras.

4 hours

4 hours

Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.

4 hours

Gain experience using techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.

4 hours

Aprende a implementar la gestión de datos distribuidos y el machine learning en Spark utilizando el paquete PySpark.

4 hours

4 hours

Learn how to approach and win competitions on Kaggle.

4 hours