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Machine Learning using Python Programming - CouponED

#artificialintelligence

Learn the core concepts of Machine Learning and its algorithms and how to implement them in Python 3 New Rating: 4.4 out of 54.4 (215 ratings) 32,564 students What you'll learn Description'Machine Learning is all about how a machine with an artificial intelligence learns like a human being' Welcome to the course on Machine Learning and Implementing it using Python 3. As the title says, this course recommends to have a basic knowledge in Python 3 to grasp the implementation part easily but it is not compulsory. This course has strong content on the core concepts of ML such as it's features, the steps involved in building a ML Model - Data Preprocessing, Finetuning the Model, Overfitting, Underfitting, Bias, Variance, Confusion Matrix and performance measures of a ML Model. We'll understand the importance of many preprocessing techniques such as Binarization, MinMaxScaler, Standard Scaler We can implement many ML Algorithms in Python using scikit-learn library in a few lines. Can't we? Yet, that won't help us to understand the algorithms. Hence, in this course, we'll first look into understanding the mathematics and concepts behind the algorithms and then, we'll implement the same in Python.


How To Implement Machine Learning Algorithm Performance Metrics From Scratch With Python

@machinelearnbot

This article was written by Jason Brownlee. Jason is the editor-in-chief at MachineLearningMastery.com.He has a Masters and PhD in Artificial Intelligence, has published books on Machine Learning and has written operational code that is running in production.


How To Implement Machine Learning Algorithm Performance Metrics From Scratch With Python

#artificialintelligence

After you make predictions, you need to know if they are any good. There are standard measures that we can use to summarize how good a set of predictions actually are. Knowing how good a set of predictions is, allows you to make estimates about how good a given machine learning model of your problem, In this tutorial, you will discover how to implement four standard prediction evaluation metrics from scratch in Python. You must estimate the quality of a set of predictions when training a machine learning model. Performance metrics like classification accuracy and root mean squared error can give you a clear objective idea of how good a set of predictions is, and in turn how good the model is that generated them.


How To Implement Machine Learning Algorithm Performance Metrics From Scratch With Python

@machinelearnbot

This article was written by Jason Brownlee. Jason is the editor-in-chief at MachineLearningMastery.com.He has a Masters and PhD in Artificial Intelligence, has published books on Machine Learning and has written operational code that is running in production. After you make predictions, you need to know if they are any good. There are standard measures that we can use to summarize how good a set of predictions actually are. Knowing how good a set of predictions is, allows you to make estimates about how good a given machine learning model of your problem, In this tutorial, you will discover how to implement four standard prediction evaluation metrics from scratch in Python.


R or Python, a practical problem

@machinelearnbot

Which technology works best in a team when we are introducing data mining. The team has been using Excel as the data analysis tool, how can we apply/ run the data mining model (such as decision tree) on excel? I have been using R for a while and enjoy it very much. Good supply of fresh grad with training in R...However, when it comes to using and running the data mining model, R does a very poor job in execution. It is such a good tool when we develop and research patterns in a laboratory where the data scientists brainstorm.