It takes you 14 days to learn how to begin using Python to build accurate predictive models and confidently complete machine learning projects. Take advantage of my referral link today and become a medium member. For just $5 a month, you will have access to everything Medium has to offer. By becoming a member, I will receive $2 from $5, which will assist me in maintaining this blog. There is a lot of important information in this post. Bookmark it if you find it useful.

Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started, build accurate models and confidently complete predictive modeling machine learning projects using Python in 14 days. This is a big and important post. You might want to bookmark it. Python Machine Learning Mini-Course Photo by Dave Young, some rights reserved.

The data features that we use to train our machine learning models have a huge influence on the performance we can achieve. Irrelevant or partially relevant features can negatively impact model performance. Feature selection is a process where we automatically select those features in our data that contribute most to the prediction variable or output in which we are interested. Having irrelevant features in our data can decrease the accuracy of many models, especially linear algorithms like linear and logistic regression. We can learn more about feature selection with scikit-learn in the article Feature selection.

Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started, build accurate models and confidently complete predictive modeling machine learning projects using Python in 14 days. This is a big and important post. You might want to bookmark it. Python Machine Learning Mini-Course Photo by Dave Young, some rights reserved.

Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started, build accurate models and confidently complete predictive modeling machine learning projects using Python in 14 days. This is a big and important post. You might want to bookmark it. Python Machine Learning Mini-Course Photo by Dave Young, some rights reserved.

Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started, build accurate models and confidently complete predictive modeling machine learning projects using Python in 14 days. This is a big and important post. You might want to bookmark it. Python Machine Learning Mini-Course Photo by Dave Young, some rights reserved.

Data is rarely clean and often you can have corrupt or missing values. It is important to identify, mark and handle missing data when developing machine learning models in order to get the very best performance. In this post you will discover how to handle missing values in your machine learning data using Weka. How To Handle Missing Data For Machine Learning in Weka Photo by Peter Sitte, some rights reserved. The problem used for this example is the Pima Indians onset of diabetes dataset.

Finding an accurate machine learning model is not the end of the project. In this post you will discover how to save and load your machine learning model in Python using scikit-learn. This allows you to save your model to file and load it later in order to make predictions. Save and Load Machine Learning Models in Python with scikit-learn Photo by Christine, some rights reserved. Pickle is the standard way of serializing objects in Python.

The data features that you use to train your machine learning models have a huge influence on the performance you can achieve. Irrelevant or partially relevant features can negatively impact model performance. In this post you will discover automatic feature selection techniques that you can use to prepare your machine learning data in python with scikit-learn. Feature Selection For Machine Learning in Python Photo by Baptiste Lafontaine, some rights reserved. Feature selection is a process where you automatically select those features in your data that contribute most to the prediction variable or output in which you are interested.