With the rapid growth in the machine learning field, sometimes it is overwhelming to keep track of all the important concepts. That is the time to bring in the machine learning cheatsheet! This blog will break down the vital terms and concepts you should know to survive in the world of machine learning. This machine learning cheatsheet will demystify advanced topics for a beginner or those looking to freshen up their memory and provide you with a concrete base.
What is a Machine Learning Cheatsheet?
A machine learning cheatsheet contains definitions, formulae, and important concepts related to machine learning. It allows you to swiftly understand and recall important topics, especially when undertaking projects, preparing for interviews, or going through complicated algorithms. Think of it as your go-to resource for brushing up on the fundamentals, no matter your level of expertise.
How to Use a Machine Learning Cheatsheet?
Treat your machine learning cheatsheet as a living document. Bookmark this page, add notes, and revisit regularly. Whether you’re learning something new or revisiting material you’ve seen before, a cheat sheet helps you reinforce what you already know. Refer to it during coding, as a study tool, or while preparing for a job interview as a personal assistant for machine learning mastery.
Machine Learning Cheatsheet: Top 20+ Key Terms You Should Know!
Ready to dive in? Following are the top 20+ key terms you will encounter day in and day out in your machine learning journey. This cheat sheet takes you through the various concepts of machine learning, ranging from simple algorithms to complex models. Let’s see each of these one by one.
1. Supervised Learning
The algorithm is trained on labeled data. In other words, with this approach, the model learns from input-output pairs a generalization method to unseen data.
2. Linear Models
Linear models are the simplest models used in machine learning. They assume a linear relationship between input features and the output variable. They usually serve as a baseline solution, as they are easily interpretable and simple to understand.
3. Linear Regression
Linear regression is a type of regression analysis that can be used in predicting continuous outcomes. The method defines the relationship between input variables or features and the output by fitting a linear equation to the data.
4. Logistic Regression
Logistic regression is a classification methodology that is utilized to predict the possibility of occurrence of an event, given that the outcome of the event is binary in nature. The name may mislead many, but it has nothing to do with regression but, in fact, is a classification technique.
5. Ridge Regression
Ridge regression is a type of linear regression where a penalty term is added to the loss function. In this way, it reduces overfitting by shrinking the coefficients of less important features.
6. Lasso Regression
Lasso regression extends the linear regression model by shrinking some coefficients all the way to zero, hence effectively selecting the important features. It is thus useful in feature selection.
7. Tree-based Models
This is a class of models that is based on decision trees. The models carve the data into subsets based on the values of the features; this helps make predictions.
8. Decision Tree
Decision tree – flowchart-like structure, in which every internal node represents a feature, every branch addresses a decision, and each leaf represents an outcome. They are relatively easy to interpret; therefore, they may suffer from some overfitting.
9. Random Forest
Random forests is another ensemble method that constructs a multitude of decision trees and integrates the outputs to produce predictions. It reduces overfitting and increases accuracy compared to a single decision tree.
10. Gradient Boosting Regression
Gradient Boosting Regression – This is an iterative method where a series of models is built and each new model corrects the errors of its predecessor. Hence, it is very efficient in regression and classification tasks.
11. Unsupervised Learning
Unsupervised learning involves the training of algorithms on data without labeled output. The aim would be to discover hidden patterns or groupings in the data without explicit supervision.
12. Clustering Models
Clustering models are utilized in unsupervised learning to group similar data points into clusters. Actually, the real use of clustering models lies in finding out any natural groupings present in any data.
13. K-means
K-means is one of the most popular clustering algorithms which segments the data into K clusters. It assigns all the data points to a cluster of the nearest mean, which produces a twist in the data by minimizing within-cluster variance.
14. Hierarchical Clustering
Hierarchical clustering produces a hierarchy of clusters in a sequence of bottom-up or top-down steps. The method does not take any a priori knowledge about the number of clusters, and a dendrogram can be obtained by visualizing the clustering process.
15. Gaussian Mixture Models
Gaussian mixture models are probabilistic models; they are utilized for modeling normally distributed subpopulations of an overall population. They are an extension from K-means clustering that can be used to soft cluster data points.
16. Association
Association is the art of finding or establishing relationships or links between variables. It does find its application in market basket analysis where, using machine learning, the association among items is detected.
17. Reinforcement Learning
Reinforcement learning is a type of learning where an agent interacts with an environment and learns to do something based on rewards or penalties obtained upon performing any action. Ultimately, the goal could be maximizing the expected cumulative rewards over time.
18. Q-learning
Q-learning is a reinforcement learning technique that learns through trial and error an optimal action to take at every possible state by learning a Q-value or quality of the action.
19. Deep Q-Networks (DQN)
Deep Q-Networks are basically an integration of Q-learning along with deep learning, wherein neural networks are used to approximate Q-values, hence they are one of the power tools for solving complex reinforcement learning problems.
20. Policy Gradient Methods
Policy gradient methods are a class of reinforcement learning algorithms that directly optimize the policy, instead of the value function, by adjusting the parameters of the policy in the direction that maximizes expected rewards.
21. Ensemble Learning
In ensemble learning, multiple models, often of the same type, combine to achieve an improvement. It reduces overfitting and enhances the accuracy of results.
22. Gradient Boosting Machines
Gradient boosting machines is an ensemble technique to build models one after the other. Each of these models is developed to correct the mistakes of the previous model. It works effectively for both regression and classification.
23. XGBoost
XGBoost is a very efficient implementation of gradient boosting that is gaining a lot of popularity because of its speed and performance. It is designed to be distributed and parallel hence it is very commonly found to be winning most competitions in machine learning.
24. LightGBM
LightGBM is a gradient-boosting framework that is based on learning of tree-based models. It is optimized for efficiency and speed; hence, it finds its applications when dealing with large datasets to attain speedy training.
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FAQs: Machine Learning Cheatsheet
Q1. What Are The Typical Machine Learning Algorithms In Use Today?
Some of the common machine learning algorithms include decision trees, support vector machines, neural networks, and gradient boosting methods.
Q2. How Should I Choose The Appropriate Machine Learning Model For My Project?
Take into consideration the problem type-customer classification, regression, clustering-and the size of your dataset and the complexity of the relationships between your variables.
Q3. What Is The Difference Between Underfitting And Overfitting In Machine Learning?
Underfitting is a case when the model is too simple to understand the pattern in data, and if it is too complex, the model is overfitting since it captures noise in the data.
Q4. How Important Is Feature Scaling In The Field Of Machine Learning?
Feature scaling is quite crucial for algorithms like SVM, K-means, and gradient descent methods because it ensures each feature weighs equally in results.
Q5. What Is Cross-Validation In Machine Learning, And Why Does It Matter?
Cross-validation is an approach to guarantee the performance of the model by splitting data into training and testing to prevent overfitting and validate its generalization.
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