Introduction
Machine Learning is a field of artificial intelligence (AI) that allows systems to learn from data and use the learned knowledge to make predictions/decisions about future data without being explicitly programmed for every task. It was introduced by Arthur Samuel in 1959 by developing the first self-learning computer programs: a checkers-playing program.
Machine Learning models are designed in such a way that instead of following the specific patterns and rules of algorithms, they use the provided data and use algorithms to find patterns and relationships in data, and then use those patterns to analyze the given problem, and give the result based on their own classification, predictions, and decisions.
Key Mechanism of Machine Learning
1. Learning from Data: Machine Learning models are trained on the provided data by allowing themselves to learn from the environment. The more good-quality and relevant data is provided to the model, the better it will give predictions on the provided problem. The training of a machine learning model depends on the provided data.
2. Improve Over Time: Machine Learning models improve over time. The more time it spends on analyzing the data, the better results it will start giving in the future because the models are self-learners, and as they work on more data, the better the results will be. It refines itself over time.
3. Not Explicitly Programmed: As compared to other models, which use a specific set of rules and patterns of algorithms to give an output, a machine learning model uses its own knowledge and experience to give the results.
Types of Machine Learning
Machine Learning (ML) can be classified into several main types based on how the model learns from data. Different types of machine learning are as follows:
1. Supervised Learning
In supervised learning, the model is trained on a labelled dataset, meaning the input data is already paired with the correct output. The model learns to map input to output and then can predict the result for new, unseen input.
Example: Classification of mails which are spam, or not spam, image classification, predicting the pricing of the house based on features such as area, location, number of rooms, etc.
2. Unsupervised Learning
In unsupervised learning, the model is trained on unlabeled data, meaning there is no particular relationship between the input and the output of the provided data, unlike in supervised learning. The algorithm provided in unsupervised learning tries to discover hidden patterns, structures, or groupings in the input data on its own.
Unsupervised learning is like a machine that is provided with a box of mixed items without telling it what they are – the machine figures it out by itself how to group or organize those items.
Example: A recommended system where a model will suggest movies that you prefer to watch based on your past experience, like Netflix or Disney+Hotstar.
3. Reinforcement Learning
In reinforcement learning, the model is trained by providing an agent that learns to make decisions by interacting with an environment. The agent will receive feedback as a response in the form of a reward or penalty, based on its experience. It tries to perform actions to maximize the number of rewards and reduce penalties over time.
Furthermor, reinforcement learning is used where an agent needs to make decisions based on feedback (rewards/penalties), and the feedback plays a major role in the learning process of an agent. It is used in the following applications.
• Gaming: Training an AI agent to play games like chess, go, and checkers. The agent also learns to play video games.
• Robotics: Training robots to walk in a provided direction, pick the selected items through its arms, and autonomous drones learning navigation and obstacle avoidance.
• Self-Driving Cars: Developing cars with auto-pilot features that make safe driving decisions, such as lane following, keeping proper control of acceleration, brake, and clutch while obeying traffic rules.
Step-by-Step Guide to Machine Learning
1) Understand the Basics of Machine Learning: Before diving deep into machine learning or starting a project, you should first learn the basics of machine learning, the key concepts of the topic, which are supervised learning, unsupervised learning, and reinforcement learning. These topics have already been defined in the above section of this article.
2) Mathematics and Programming: After you have learn the basics of machine learning, like what is machine learning, how the model in machine learning is trained, and key concepts, you need to brush up on your mathematics topics, which are linear algebra, statistics, and calculus.
Once you have brushed up on these mathematical topics, you can start with a programming language. Python is a very popular programming language for machine learning as it supports various libraries like pandas, NumPy, and Matplotlib, which are used a lot in machine learning projects.
3) Choosing the right tools: Before working on projects, it is essential to choose the right tools for your project, which can improve your productivity and give better results in the development of projects. Following is the list of tools that can be used
• Jupiter Notebook: For coding and visualization.
• TensorFlow: Framework for building machine learning models.
• Scikit-learn: For preprocessing data and implementing algorithms.
4) Prepare Your Dataset: To obtain better results in your machine learning model, you should choose a dataset that is clean and relevant. For collecting the data, you can use open datasets from platforms like Kaggle, UCI ML Repository, or Google Dataset Search. You should split your dataset into categories like training, validation, and test sets.
5) Choose a Machine Learning Model: You should choose your machine learning model based on the problem you are solving.
• Linear Regression: Linear regression is used in machine learning when the goal is to predict a continuous target variable based on one or more independent (input) variables. It builds a linear relationship between the input variable and draws the “best-fit” line using the data provided to forecast future outcomes, such as predicting the pricing of a house based on area, location, and market analysis.
• Logistic Regression: Logistic Regression is used in machine learning in scenarios where the output variable is discrete rather than continuous. Logistic Regression is used in binary classification when the output has two possible categories (Yes/No, 0/1, True/False).
Examples of logistic regression in binary classification include predicting if a student will pass/fail an exam, classification of spam or not spam email, and predicting disease diagnosis (positive/negative).
Logistic regression is not used in scenarios where the relationship between the dependent variable is continuous (example: predicting salary, house price), or when the dataset has complex nonlinear relationships (example: predicting deep patterns in image, speech data).
• Decision Trees/Random Forests: Decision Trees/Random Forests are used for non-linear problems. Decision Tree is used when you need classification tasks (example: approve/reject loan), regression tasks (example: predicting house prices, sales revenue). It works well with mixed data types, with both categorical (example: color, gender) and numerical features.
Random Forests is used in machine learning when you need higher accuracy and stable results compared to a single tree. It is used with complex datasets with many features (example: fraud detection, gene expression classification). Random forests are used for handling missing values or inappropriate data.
• Neural Networks: Neural networks are used for performing more complex tasks in machine learning, like image processing, speech recognition. It is a powerful tool, but not always the first choice for building machine learning models. It is used when logistic regression, decision trees, and Random forests are not enough to solve a particular problem.
Neural networks are used in machine learning to solve problems like image-related tasks (image classification, object detection, face recognition), natural language processing (sentiment analysis, language translation (English to Hindi), chatbots, speech-to-text, and text summarization), and Sequential or Time Series Data (example: stock market prediction, Weather forecasting, and machine failure prediction).
6) Train Your Model: Once the coding part is done for the machine learning model, you should start initializing your data into the model to test and see the result. Use the training dataset to adjust the model’s parameters by minimizing the error (example: Mean squared error for regression).
Training is usually done using optimization algorithms like Gradient Descent, across multiple iterations (one complete pass of the entire training dataset) until the model converges. To avoid overfitting, the model’s performance is also checked on a validation set.
7) Evaluate Your Model: Use the validation dataset to test the model’s accuracy. In this practice, common metrics include accuracy, precision, recall, F1 score, and ROC-AUC. You can use regression metrics in case of a non-classification model, which uses Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R^2 score.
For generalization check, monitor the performance of the model not only on the training data but also on unseen validation/test data (to avoid overfitting).
8) Fine-Tune and Optimize your model: Experiment with hyperparameter tuning using GridSearchCV or RandomizedSearchCV. Reduce overfitting with regulation techniques (such as L1/L2 regularization, dropout, or pruning), or by increasing the amount of training data.
9) Test and Deploy: Once the model is prepared, test your model on unseen data to ensure its effectiveness. Deploy your model using platforms like Flask, Django, or cloud servers such as AWS, Google Cloud, or Azure for large-scale and production-level deployment. Continuous monitoring is important after deployment of your project to maintain performance on real-world data.
Conclusion
This article describes in detail about the Machine Learning, starting from the basics, key mechanisms, how things in machine learning work, and the proper tools you can choose for enhancing your productivity in machine learning.
I hope this article has provided you a valuable information about the topic. If you are looking for more such kind of information, I suggest you visit the Tpoint Tech Website, where you can find various articles on programming and other technology, as well along with interview questions, working examples with their explanations, and an online compiler where you can run your code easily.