📄️ Codebase
Use with KNIME:
📄️ Week 1: Introduction to Machine Learning with Big Data
Machine learning is the field of study that focuses on computer systems that can learn from data. That is the system's often called models can learn to perform a specific task by analyzing lots of examples for a particular problem. For example, a machine learning model can learn to recognize an image of a cat by being shown lots and lots of images of cats. This notion of learning from data means that a machine learning model can learn a specific task without being explicitly programmed. In other words, the machine learning model is not given the step by step instructions on how to recognize the image of a cat. Instead, the model learns what features are important in determining whether it picture contains a cat from the data that has analyzed. Because the model learns to perform this task from data it's good to know that the amount and quality of data available for building the model are important factors in how well the model learns the task.
📄️ Week 2: Data Exploration & Preparation
A sample is an instance or example of an entity in your data. This is typically a row in your dataset. This figure shows part of a dataset of values related to weather. Each row is a sample representing weather data for particular day. The table in the figure shows four samples of weather data, each for different day. In this table, each sample has five values associated with it. These values are different information pieces about the sample such as the sample ID, sample date, minimum temperature, maximum temperature, and rainfall on that day. We call these different values variables of the sample.
📄️ Week 3: Classification
Classification is one type of machine learning problems. In the classification problem, the input data is presented to the machine learning model and the task is to predict the target corresponding to the input data. The target is a categorical variable, so the classification task is to predict the category or label of the target given the input data.
📄️ Week 4: Evaluation of Machine Learning Models
Recall that a machine learning model maps the input it receives to an output. For a classification model, the model's output is the predicted class label for the input variables and the true class label is the target. Then if the classifier predicts the correct classes label for a sample, that is a success. If the predicted class label is different from the true class label, then that is an error. The error rate, then, is the percentage of errors made over the entire data set. That is, it is the number of errors divided by the total number of samples in a data set. Error rate, or simply error, on the training data is refered to as training error, and the error on test data is referred to as test error.
📄️ Week 5: Regression, Cluster Analysis and Association Analysis
Linear Regression