One Pager Cheat Sheet

• By exploring NumPy and learning about various classes and keywords, this lesson will provide you with a fundamental introduction to machine learning.
• We can use random to generate a guess, use an infinite loop to continually update it and track the direction of the jump and its size to eventually guess the user's number.
• By understanding the internal state, loss, learning rate, optimizer, convergence threshold, learning iteration, epoch and accuracy, you can create a machine learning algorithm to optimize an objective function with the use of an activation function.
• Optimizers are part of the machine learning algorithm which work to minimize the loss by updating the weights in order to make the loss function smaller and get more accurate predictions.
• Data is divided into the categories of Training, Test, Feature, and Label, with the Actual Train Data and Cross-Validation Data being two subsets of the Training data which helps to test the accuracy of the model by exposing it to both seen and unseen data.
• Machine Learning consists of two main classes: Supervised Learning, which uses labeled data, and Unsupervised Learning, which does not require labels and is more difficult to measure the "goodness" of a model.
• Machine learning can be divided into two categories; instance-based learning, which does not save weights for later use, and model-based learning, which saves weights for future use and can be improved upon with additional data.
• Machine learning algorithms can be divided into two categories: online learning, where data is given live while the training loop is running, and offline, or batch, learning, where data is given all at once.
• Unsupervised ML is a type of Machine Learning algorithm that discovers patterns or similarities within data sets without the need for labeled data.
• Understanding the fundamentals of machine learning is essential to succeeding with machine learning, and it is recommended to go through the example algorithm multiple times to gain a better understanding.