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HomeBlogData ScienceTop 10 Machine Learning Techniques
Machine learning is a part of AI (artificial intelligence) that focuses on developing various algorithms and models to make decisions without human intervention. Various algorithms and models are trained using machine learning techniques like supervised, unsupervised, reinforcement, etc.
In this article, we delve into the top 10 machine learning techniques that have gained immense popularity and have proven to be game-changers in the field. From classical algorithms like linear regression to cutting-edge algorithms like deep learning and reinforcement learning, these techniques enable you to extract insights and unlock the true potential of AI.
Machine learning involves multiple steps: data collection, data processing, model training, evaluation, and decision-making.
These five essential steps are crucial in how machine learning operates to train any given model for various machine learning methods. Let us briefly understand the steps involved in machine learning:
Data Collection: This step involves collecting relevant data related to the problem we want the computer to solve. For example, we want to build a model to predict whether a patient has a specific health condition. In that case, we will want to collect related attributes like age, sex, health risks, history, etc.
Data Preprocessing: Before training the model, we must clean and preprocess the data. This involves removing errors, handling missing values, and transforming the data into a suitable format for the model.
Training: In this step, machine learning techniques are used to train the models based on the preprocessed data. In training the models, various hyperparameters are varied and changed accordingly to develop a mathematical function.
Evaluation: After training, a separate set of data, called a test set, measures how well the model can make predictions or decisions based on new, unseen data. The model's accuracy, precision, or other performance metrics are assessed to determine its effectiveness.
Decision-making: The final step in which the trained and evaluated model is ready to take in new data and make informed decisions based on the mathematical function.
These are the five steps involved in machine learning, but with the ever-increasing complexities of data and the need to develop a robust model, following only these basic steps would be insufficient. You need to have a solid understanding of the underlying concepts. The best course for Machine Learning will help you learn and transition to Data Science roles with career coaching, and networking opportunities.
Regression techniques in machine learning are used to predict numerical values or continuous outcomes. It establishes a relationship between input variables (independent variables or features) and a target (dependent) variable.
Regression has mainly two types:
a. Linear Regression: This type of ML technique is used to predict continuous variables by creating a straight line that best fits the input data points and predicting the value of one variable based on the value of the other. The best-fit line is drawn, considering that the distance between the best-fit line and the data points must be minimal.
b. Logistic Regression: Logistic regression is one of the machine learning techniques for prediction. It is used to predict binary outcomes or categorical outcomes with two classes. The mathematical function used is the sigmoid function, which gives you the probability of belonging to a particular class.
Classification is a machine learning technique that predicts categorical outcomes or class labels based on input variables. It uses the input data (independent variable) to segregate the output variable (dependent variable) into specific classes.
Some of the standard classification techniques are:
a. Decision Trees: This ML technique divides the data set into branches based on the various attributes and creates a tree-like structure. Each internal node represents a test on an attribute, and each leaf node represents a class label.
b. Random Forest: Random Forest is one of the ensemble techniques in machine learning that combines multiple decision trees. It creates a diverse set of trees by training on random subsets of the data and features. The final classification is done by aggregating all the trees.
Random Forest is also a widely used machine learning technique for data analysis tasks like Feature selection, overfitting detection, and outlier detection.
c. Support Vector Machines (SVM): This is one of the types of machine learning techniques that classifies the data points by creating a hyperplane that separates the different classes so that the distance between any two classes is maximum. SVM uses kernel functions (linear, radial, etc.) to create the hyperplanes.
Some other widely used machine learning techniques are:
Clustering is one of the unsupervised learning techniques used to group similar data points based on their characteristics or patterns. Different approaches are used for determining the clusters: proximity, density-based, and probabilistic modeling. One can use clustering algorithms in real-world scenarios like customer segmentation and recommendation systems.
Some of the clustering methods are:
Some of the standard Clustering machine-learning techniques are:
Dimensionality reduction is an unsupervised machine learning technique that aims to reduce the number of features or attributes without significantly affecting the model's performance. It retains all the crucial characteristics of the model, thus improving computational efficiency and enhancing visualization.
Some of the machine learning techniques used for dimensionality reduction are:
Ensemble machine learning techniques combine multiple machine learning models to make more accurate predictions or decisions. For example, if, for a particular decision, multiple models are made to give results, and then a majority vote is considered to make a decision, in such cases, the probability that the collective decision is correct would be much higher.
Some of the commonly used ensemble techniques in machine learning are:
Neural networks and deep learning are machine learning methods that mimic a human brain's decision-making processes to solve complex problems.
Transfer learning is an ML technique in which an existing model's training is leveraged to improve the model further for accuracy and efficiency rather than starting from scratch. A pre-trained model trained on a large dataset for a source task is used as a starting point. The knowledge and learned representations in the pre-trained model are then transferred to a new model that is fine-tuned or adapted for the target task with a smaller dataset.
For example, through our schooling days, as we go up in grades, we build upon our existing knowledge base rather than starting from scratch. Every year, we learn new skills based on the knowledge we obtained in previous grades. This is how transfer learning works in machine learning algorithms.
Natural language processing focuses on creating a gateway between the natural human language and computers. Since computers don't understand natural human language, models must be developed to read, process, and understand the natural human language and, in some cases, generate it too. Numerous voice assistants use NLP to respond to human queries. NLP is a very dynamic technique and is often used in conjunction with other techniques.
Some critical tasks in Natural Language Processing are:
Word embedding is a technique that represents words as numerical vectors in a way that captures their meanings and relationships. Instead of just representing words as one-hot vectors, word embedding assigns each word a set of numbers that capture its context and semantic information.
Imagine an extensive collection of text, like books or articles. Word embedding algorithms analyze this text and learn to assign each word a vector representation in a high-dimensional space. Words that have similar meanings or are often used together end up with similar vector representations. In contrast, words with different meanings have dissimilar vectors.
Anomaly detection is used to detect any significant deviation of the data from the standard set of data points. Anomalies in the data could signify incorrect data or some unique event in the data set. The process for anomaly detection starts by training a particular model on standard data and then using that model to identify the anomalies.
There are three types of Anomaly detection:
Anomaly detection is applicable in many domains, including fraud detection, network intrusion detection, system monitoring, and quality control. The best online Data Science courses will aid you to hone your skills and take your learning to the next level.
Supervised learning is an ML technique in which machine learning models are trained on labeled data. The training dataset comprises the independent (features ) and the dependent variables ( Output ). In supervised learning, the aim is to train the model and develop a mathematical function that maps the output to the corresponding input variables.
Examples of supervised machine learning techniques include linear regression, logistic regression, KNN clustering, decision trees, random forest, etc.
Unsupervised learning is a machine learning approach in which the model learns from unlabeled data without specific output labels or target values. The goal is to discover patterns, structures, or relationships within the data without prior knowledge or guidance. The model identifies inherent similarities, clusters, or distributions in the data to gain insights or extract meaningful representations. Some unsupervised machine learning techniques examples include clustering, anomaly detection, and dimensionality reduction.
Semi-supervised learning is an amalgamation of both supervised as well as unsupervised learning. The data contains a small amount of labeled data to train the model, and the model uses the rest unlabeled data to find patterns in the data as an unsupervised learning approach. The model learns from labeled and unlabeled examples to improve its performance and generalize to unseen data.
Reinforcement learning is a type of learning in which the model learns through trial and error. The model is punished for each incorrect decision and is rewarded if the correct decision is given. This way, the model, over time, learns to maximize the rewards hence learning to provide accurate results, thus improving the model's performance.
To sum up, these were the top 10 machine learning techniques used in the industry. But the caveat here is that Machine learning is an ever-evolving field with new techniques being introduced for model creation, enhancing the capabilities of the models significantly. Today every industry needs to have data-driven decision-making processes, and here KnowledgeHut best course for Machine Learning could be the next big thing for you. Through this course, you can ace key concepts and fundamentals of Deep Learning and Machine Learning.
Machine learning techniques include supervised, unsupervised, semi-supervised, and reinforced learning techniques to train the various models. The data sets used in these techniques are different, and each model has its own set of pros and cons, and it depends on the need for these techniques to be used.
There is no best machine learning technique because it has its own set of usage patterns. It squarely depends on the nature of the problem, the data, and other associated factors that help determine the best machine-learning technique for a particular situation.
The five steps of machine learning through which models are trained are Data Collection, data Preprocessing, Training the models, Evaluation, Decision Making
Machine learning techniques are essential in training the models to do automated data analysis, prediction, and decision-making. This helps uncover inherent data patterns and can help organizations. Make effective decisions and unlock their competitive advantage.
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