2 Must-Have Machine Learning Cheat Sheets for Beginners
Machine Learning is considered to be one of the most sophisticated and lucrative careers in the world of technology. However, learning this skill can be overwhelming, especially for beginners. The good news is that there are several machine learning resources available online that can help you simplify the learning curve, including machine learning cheat sheets. In this article, we will discuss the two must-have machine learning cheat sheets for beginners.
1. Scikit-Learn
Scikit-Learn is a popular machine learning library used for supervised and unsupervised learning. It provides a range of classification, regression, and clustering algorithms, making machine learning more accessible to people with minimal experience and knowledge. The Scikit-Learn cheat sheet is a comprehensive resource that highlights the most common functions used in machine learning. It also provides an easy-to-understand guide on how to approach different scenarios while working with Scikit-Learn.
The cheat sheet is divided into the following categories:
Data Preprocessing:
Preparing data before feeding it into the model is a crucial step in the machine learning process. This section highlights the various techniques used in data preparation and cleansing, such as normalization, standardization, and encoding.
Supervised Learning:
Supervised learning refers to the method of teaching the machine to recognize patterns and predict outcomes from labeled data. This section provides an overview of the most common algorithms used in supervised learning, such as regression, decision trees, and random forests.
Unsupervised Learning:
In unsupervised learning, the machine is trained to identify patterns and detect anomalies in unlabeled data. This section covers the most used unsupervised algorithms such as clustering and dimensionality reduction.
2. Keras
Keras is an open-source software library used for building neural networks. It provides a simple and user-friendly interface that simplifies the process of developing deep learning models. The Keras cheat sheet is an excellent tool for beginners, highlighting the different layers used to build a neural network. It also provides tips on choosing the right activation function, choosing the optimizer, and defining the loss function.
The cheat sheet is divided into the following categories:
Sequential Model:
This section introduces the sequential model, a linear stack of layers used to build a neural network.
Layers:
Layers are the building block of neural networks; this section highlights commonly used layers such as dense, convolutional, and pooling layers.
Activations:
An activation function introduces non-linearity to the neural network. This section provides an overview of commonly used activation functions such as sigmoid, ReLU, and softmax.
Optimizers:
Optimizers are used to minimize the loss function and help the neural network learn faster; this section explains optimizers such as Adam and SGD.
Loss Functions:
Loss functions describe the degree of error between predicted and actual values. This section highlights commonly used loss functions such as binary cross-entropy and mean squared error.
Conclusion:
Machine learning cheat sheets are a valuable resource for beginners learning machine learning. They provide comprehensive and easy to understand guides on how to approach various scenarios while working with machine learning libraries. The Scikit-Learn and Keras cheat sheets are a must-have for beginners due to their comprehensiveness and relevance. With these cheat sheets, beginners can quickly grasp the concepts of machine learning and move closer to becoming proficient in this lucrative technology career.
