Machine learning was the first spark that showed the power of AI to the world and one of the successful methods still used to achieve AI. In recent years, machine learning changed various industries and our daily lives rapidly. In this article, we will explore the fundamentals of machine learning and its applications.
What is machine learning?
Machine learning is one of the old methods that is used to achieve AI successfully. The concept of machine learning was first introduced by Arthur Samuel in 1959, who defined it as “the ability of computers to learn data and improve their performance on specific tasks without being explicitly programmed”.
For the first time, Machine learning allowed us to overcome the limitations of traditional rule-based programming and harness the power of AI. In the past few years, machine learning has experienced significant technical progress, resulting in its widespread use. But with the emergence of neural networks, AI developers are turning to deep learning methods to build more powerful AI Systems.
“I like to refer to the past machine learning Era as AI 1.0 and the modern deep learning AI Era as AI 2.0”
How does machine learning work?
Generally, AI learns the same way humans learn. AI learns how to perform a task by seeing examples of data related to that task. Here AI tries to find out patterns and relationships in that data.
Machine learning uses a special kind of learning algorithm (machine learning algorithm) to find the patterns in data. When we show a vast amount of data to the machine learning model, the AI model tries to adjust its machine learning algorithm to align with patterns in the data. Here, when we show data, we additionally say what kind of critical points in the data machine learning should consider when adjusting the algorithm. We called these key things/points in the data as features. After the training, we can use this trained machine learning AI model on new/unseen data.
To gain a better understanding of machine learning, let’s delve into its key components and steps:
Components of Machine Learning
Data
We use data to train and test our model. They can be labeled or unlabeled data. It depends on the type of machine learning algorithm(supervised or unsupervised) used. Also, we divide our dataset into Traning Data and Test Data. Training Data can be divided further for a better training process.
Learning Algorithm
Machine Learning Algorithm is an algorithm used to learn from data. These Learning algorithms try to find the patterns and linear or non-linear relationships between given data and their results. Nowadays There are Lots of machine learning algorithms out there, Among them Linear Regression, Logistic Regression, Decision Trees, and Naive Base are the most common. These algorithms can be divided into two categories Supervised Algorithms and Unsupervised Algorithms.
Features
Features(also known as variables or attributes) are the measurable parameters or properties of the data set used to train the ML model. In machine learning, features are selected manually by us from the data set and serve as inputs to the machine learning algorithms.
For example, imagine we have to predict the house price and we have labeled data: House_No, Build_Year, SquareFeets, No_Rooms, No_baths, Outdoor_paint, Garden_size, Garadge_size, and Sales_Price. Now here we use only Build_Year, SquareFeets, No_Rooms, No_baths, Garden_size, and Garadge_size to train our AI. These selected data are called the features.
The choice and quality of features can significantly impact the performance and accuracy of the models. So Choosing the right features is a critical process in machine learning and it is one of the weak points or limitations in machine learning.
Loss Function
The loss function, also known as the cost function or objective function is used to quantify the difference between the predicted values and the true values. The goal is to minimize this cost function during the training process to obtain the best-fitting line or hyperplane. Mean Squared Error(MSE), Mean Absolute Error(MAE), Softmax Loss, and Log Loss are the most common cost functions in the machine learning field. The choice of the cost function depends on used machine learning algorithm and the task.
For example, MSE and MAE are used in regression tasks. Binary Cross-Entropy Loss(Log Loss) is used in binary classification tasks and Softmax Loss is used for multiclass classification tasks.
Optimization Algorithm
The optimization algorithm is used to find the best-fitting line or hyperplane that minimizes the prediction error in the machine learning algorithm. Simply, this is the method we are using to upgrade our learning algorithm parameters for minimizing the loss function. There are lots of optimization algorithms out there. We should select a suitable optimization algorithm based on our machine learning algorithm and the task.
For example, The optimization algorithm used in linear regression is typically the least squares method, which minimizes the sum of the squared differences between the predicted values and the actual values in the training data.
Hyperparameters
Hyperparameters are the constant parameters that control various aspects of the learning algorithm and can significantly affect the performance and behavior of the model. These are set before the learning process begins and are not learned from the data. Some common hyperparameters in machine learning include Regularization Parameters, Kernel Parameters, Number of Clusters, Number of Trees, Maximum Tree Depth, Number of Neighbors (k), and Learning Rate. It is important to note that different learning algorithms use different hyperparameters and their values.
Steps in Machine Learning
Data gathering & Preparing
The first step is Data gathering & preparation. To train accurate and trustworthy AI models, high-quality data is very important. Data can be collected from various sources and before they are used for the training it is essential to ensure that they are clean by removing non-valued data. Otherwise, they will affect the machine learning model’s performance and accuracy badly.
Feature Extraction
As I said, In machine learning feature extraction or feature selection is done manually. Effective feature extraction can significantly improve the performance and efficiency of machine learning models. So this is also a very important step in ML.
Choosing an Algorithm
The choice of a Machine Learning algorithm depends on what kind of task you are going to do.
For example, Regression models like Linear regression, and Random Forest are commonly used in regression tasks like predictions. Algorithms like Logistic regression, K-nearest neighbors (KNN), and Naive Bayes are used in classification tasks.
Training
This is the step of fitting the machine learning model to the data. this step involves feeding data into the algorithm and adjusting the model’s parameters using optimization algorithms to minimize the loss function. During training, the model learns the underlying patterns and relationships in the data.
Evaluation
Once a model has been trained, it’s necessary to evaluate its performance and generalization abilities. (How well it performs on unseen data). This evaluation entails applying the model to a distinct dataset that wasn’t used during training – known as the test set. The purpose of this evaluation is to determine the model’s effectiveness at predicting outcomes on new, unseen data.
Hyperparameter Tuning
Hyperparameter tuning, also known as fine-tuning, is the process of adjusting parameters that are not learned during training, like learning rate, and regularization strength.
Machine Learning Algorithms
Linear Regression
Linear regression is a machine learning algorithm that finds a linear relationship between a dependent variable and one or more independent variables in AI. Most commonly Linear Regression is used for predictions and forecasting of market prices and trends.
Logistic Regression
Logistic regression is also a machine learning algorithm that is used for predictions and classification tasks in AI. For example, grouping photos and fraud detection by identifying data anomalies.
Decision Trees
Decision trees are popular non-parametric (flexible machine learning approaches that do not make strong assumptions about the underlying data distribution) supervised machine learning algorithms that are used for both classification and regression tasks. They create a flowchart-like structure to make decisions based on feature values.
Support Vector Machines (SVM)
Support Vector Machines (SVM) is a powerful supervised machine learning algorithm used for both classification and regression tasks. SVMs find a hyperplane that separates data points of different classes with the largest margin.
Naive Bayes
Naive Bayes is a simple yet powerful machine learning algorithm based on Bayes’ theorem and the assumption of feature independence. It assumes that features are independent of each other. This algorithm is commonly used for classification tasks and it is able to handle text classification problems like spam filtering or sentiment analysis very effectively.
K-Nearest Neighbors (KNN)
K-Nearest Neighbors (KNN) is a popular supervised machine learning algorithm for classification and regression tasks. It is a non-parametric algorithm that makes predictions based on the similarity of instances in the feature space and the majority vote of their nearest neighbors in the feature space.
Use of Machine Learning
Regression Tasks
Machine learning is highly used for Regression tasks where predict a continuous numerical value based on input features. This is very similar to classification, except for the format of the output.
For example, Machine learning can be used to predict future housing prices based on features like location, size, and number of rooms. These kinds of predictions are also used for algorithmic trading where we predict the future value of a specific market (like the Crypto market).
Classification Tasks
Classification is another use of machine learning. Here machine learning algorithms classify data into different categories or classes. There are both types of supervised and unsupervised machine learning algorithms that can be used for classification. However, the choice of machine learning algorithm depends on the task.
For instance, You can use Machine learning to classify emails as spam or not spam or to predict whether a customer will buy or not based on their past behavior. Also, object recognition is another classification task where the input is an image (usually described as a set of pixel brightness values), and the output is a numeric code identifying the object in the image.
Language Translation
Machine Learning can be used to translate text from one language to another automatically. These models are trained on large amounts of bilingual text data and can be used for translating documents, websites, or real-time communication. For example, translating a paragraph from English to French via Google Translator.
Anomaly detection
Machine learning can be used for anomaly detection, where the goal is to identify unusual or abnormal patterns in data. This is very useful in many areas, such as fraud detection in financial transactions, network intrusion detection, or equipment failure prediction in industrial systems.
Synthesis and sampling
In this case, the Machine Learning algorithm is tasked with producing fresh examples that are similar to those in the training set. This is very helpful for media applications where having an artist manually create large volumes of content can be expensive or boring. Now we can generate any art or image by simple coding rather than drawing by hand. This is very helpful for improving our productivity.
Major Challenges of Machine Learning
- Feature Engineering: In machine learning, We have to select features from raw data manualy. It often requires domain knowledge and manual effort to extract meaningful features, which can be challenging, and time-consuming. As Our machine learning model’s accuracy heavily depends on the features we selected, this is a very crucial step and one of the significant limitations of machine learning.
- Data quality and preprocessing: Machine learning heavily relies on data quality. Models can be biased or inaccurate as a result of noisy, lacking, or unrepresentative data. Data preprocessing, which includes cleaning, normalization, and handling missing values, is crucial but time- and resource-intensive.
- Overfitting: Overfitting occurs when a model becomes too closely adapted to the training data, resulting in poor performance on unseen data. This happens when the model captures noise or irrelevant patterns instead of the underlying true patterns.
Though there are some limitations in machine learning, Due to its efficiency, low hardware requirements, and simple process compared to deep learning, AI developers still widely use machine learning for AI. Especially in small and medium AI projects as it is easy to train machine learning models rather than deep learning models.