Top 15 Machine Learning Algorithms Every Data Scientist Must Know
Working with data to derive insights and create predictions is key in data science and Machine Learning (ML). Hence, data science machine learning algorithms are useful while collecting data, cleaning and preparing data, model training, model evaluation, retraining, and predicting.
In data science, insights are derived from structured and unstructured data using scientific methods, procedures, algorithms, and systems. This information helps in making business choices or resolving challenging issues.
Meanwhile, machine learning uses statistical models and algorithms to help computers learn from data and better complete tasks without explicit programming. These algorithms—trained using large datasets—can find patterns, relationships, and correlations between variables. They can then utilize this information to predict or decide based on incoming data. That is why these data science machine learning algorithms are important. Let us now dive into how they work and the top algorithms data scientists should know.
How Does Data Science Machine Learning Work?
Data science machine learning employs a variety of algorithms, techniques, and tools to draw conclusions and make predictions. The general steps in the data science machine learning process are as follows:
- Problem Overview: The first step is identifying the issue data scientists seek to tackle. This could involve everything from identifying credit card theft to foreseeing client attrition.
- Data Gathering: After defining the issue, a data scientist must gather the information needed to address it. This might entail gathering information from many sources, including databases, APIs, and outside providers.
- Data Preprocessing: The data must be cleaned and transformed into a suitable format before data scientists can train machine learning models. This might involve scaling the data, addressing missing data, and encoding categorical variables.
- Model Selection: Data scientists must choose the best machine-learning approach to address the issue after preprocessing the data. Choosing from various methods, such as decision trees, logistic regression, or neural networks, may be required.
- Model Training: Once the best algorithm has been chosen, the model must be trained using the preprocessed data. This requires supplying the algorithm with the data and modifying the model’s parameters to enhance performance.
- Model Evaluation: Data scientists must assess the model’s performance after training using a different data set that was not utilized for training. Metrics like recall, precision, and accuracy may be used in this.
- Model Execution: The model can be deployed in a production setting and used to generate predictions or choices based on new data once it has been assessed and found suitable for usage.
- Updating and Watching: To ensure the model keeps performing effectively and staying accurate when it is put into production, it must be maintained and updated over time.
15 Common Machine Learning Algorithms for Data Scientists
1. Linear Regression
Linear regression is useful for predicting the dependent variable’s value with the independent variable’s help. It helps model the relationship between a dependent and explanatory variable by expressing the observed data points on a linear equation.
2. Logistic Regression
Logistic regression is applicable for discrete values. The data science machine learning algorithm can help find the most common application for solving binary classification problems. A non-linear logistic function converts predicted values into the range of 0 to 1.
3. Hypothesis Testing
Hypothesis testing involves performing statistical tests to determine the validity of a hypothesis. Data scientists accept or reject a hypothesis according to the outcomes of the statistical test. Hypothesis testing can help determine whether an event is a trend or has occurred by chance.
4. Naive Bayes
The Naive Bayes algorithm is useful for developing predictive models. In other words, this data science machine learning algorithm is applicable for calculating the probability of an event’s occurrence in the future. The Naive Bayes framework believes that every feature is independent and contributes toward the final result.
5. Neural Networks
Neural networks can identify patterns in complex data to forecast and classify data points. These networks are organized in layers and include many interconnected nodes. The network observes the patterns via a specific “input layer.” The input layer communicates with several hidden layers where the processing occurs.
6. Support Vector Machine
Support Vector Machine (SVM) is a supervised algorithm with application in regression and classification problems. The SVM method uses a hyperplane to classify data points.
7. Conjoint Analysis
Conjoint analysis is a data science algorithm used in market research to detect customer preferences for different product attributes. Moreover, it helps identify features that customers would prefer for certain prices. Therefore, the data science machine learning algorithm is extremely useful for new product design or pricing strategies.
8. ANOVA
ANOVA, or one-way analysis of variance, helps determine whether the mean of more than two datasets is considerably different. The technique involves assessing whether all the groups of datasets are part of one large population.
9. Decision Trees
Decision trees are useful for solving prediction and classification issues. Moreover, this data science machine learning algorithm ensures that data scientists can better comprehend the data and make more accurate predictions.
A decision tree consists of nodes, links, and leaves. Each node, link, and leaf represents a feature, a decision, and a class label or outcome. However, overfitting is a major issue of the decision trees framework.
10. K-Nearest Neighbors (KNN)
KNN is a data science machine learning algorithm that uses regression and classification problems. The KNN algorithm treats an entire dataset as a training dataset. After training a model with the KNN algorithm, data scientists aim to predict the result of a new data point. Since KNN is a non-parametric algorithm, it does not assume anything about the underlying data.
11. Principal Component Analysis
Principal Component Analysis (PCA) involves evaluating data from the perspective of a principal component, which is the direction with the largest variance. Furthermore, PCA analysis revolves around rotating the axis of every variable to a higher Eigenvalue/Eigenvector pair and defining the prime components.
12. Ensemble Methods
The ensemble method principle believes several weak learners can collaborate to offer a strong prediction. Ensemble methods can reduce the bias and variance of a particular machine learning model. However, several models are accurate in some circumstances and inaccurate in others. But when the two models are combined, the predictions get balanced out.
13. Clustering
The clustering technique involves grouping a dataset into unique, segmented clusters. Additionally, since the output remains unknown to the analyst, clustering is an unsupervised data science machine learning algorithm called unsupervised classification. In this method, data scientists wait for the algorithm to define the output.
14. Random Forests
Random forests can solve the overfitting of decision trees and regression and classification problems. Based on the principle of ensemble learning, the method involves assessing predictions of many individual decision trees to deliver the final result.
For example, consider a random forest with seven decision trees and two classes labeled A and B. Four may have voted for class A and five for class B. Therefore, the model will predict class B since B has received more votes.
15. Reinforcement Learning
The Reinforcement Learning (RL) algorithm is useful when there is a lack of historical data related to a problem. Unlike traditional machine learning methods, RL is useful because it does not demand information in advance. The RL framework allows you to learn from data as you progress, and it is particularly successful for games.
Top Skills Necessary to Pursue a Data Science Machine Learning Career
1. Programming
Data scientists must know programming languages like Java, SQL, Python, and Perl. They enable data scientists to organize unstructured datasets.
2. Web Scraping
Web scraping involves automated data extraction from web pages.
3. Probability and Statistics
Probability and statistics are based on studying randomness and uncertainty. It refers to using mathematical tools for decision-making.
4. Database Management
Database management involves arranging, accessing, and saving data in a database system.
5. Business Intelligence
Business intelligence entails using tools and techniques for data analysis to make the best business decisions.
Why are Emeritus Data Science Courses so Popular in India?
The data science courses offered by Emeritus can help you become career-ready. The courses will offer theoretical as well as practical knowledge through hands-on experience. According to Glassdoor, earning a certification from these courses can help you build a lucrative career since the average annual salary in this field in India is 12 lakh. So enroll to learn more about the importance of data science, machine learning, and other related aspects of this growing profession.
