What is Machine Learning?
Arthur Samuel, a pioneer in the field of artificial intelligence and computer gaming, coined the term “Machine Learning”. He defined machine learning as – a “Field of study that gives computers the capability to learn without being explicitly programmed”. In a very layman’s manner, Machine Learning(ML) can be explained as automating and improving the learning process of computers based on their experiences without being actually programmed i.e. without any human assistance. The process starts with feeding good quality data and then training our machines(computers) by building machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data we have and what kind of task we are trying to automate.
What is Machine Learning?
Machine Learning is a branch of artificial intelligence that develops algorithms by learning the hidden patterns of the datasets used it to make predictions on new similar type data, without being explicitly programmed for each task.
Traditional Machine Learning combines data with statistical tools to predict an output that can be used to make actionable insights.
Machine learning is used in many different applications, from image and speech recognition to natural language processing, recommendation systems, fraud detection, portfolio optimization, automated task, and so on. Machine learning models are also used to power autonomous vehicles, drones, and robots, making them more intelligent and adaptable to changing environments.
A typical machine learning tasks are to provide a recommendation. Recommender systems are a common application of machine learning, and they use historical data to provide personalized recommendations to users. In the case of Netflix, the system uses a combination of collaborative filtering and content-based filtering to recommend movies and TV shows to users based on their viewing history, ratings, and other factors such as genre preferences.
Reinforcement learning is another type of machine learning that can be used to improve recommendation-based systems. In reinforcement learning, an agent learns to make decisions based on feedback from its environment, and this feedback can be used to improve the recommendations provided to users. For example, the system could track how often a user watches a recommended movie and use this feedback to adjust the recommendations in the future.
Personalized recommendations based on machine learning have become increasingly popular in many industries, including e-commerce, social edia, and online advertising, as they can provide a better user experience and increase engagement with the platform or service.
The breakthrough comes with the idea that a machine can singularly learn from the data (i.e., an example) to produce accurate results. Machine learning is closely related to data mining and Data Science. The machine receives data as input and uses an algorithm to formulate answers.
Difference between Machine Learning and Traditional Programming
The Difference between Machine Learning and Traditional Programming is as follows:
|Machine Learning is a subset of artificial intelligence(AI) that focus on learning from data to develop an algorithm that can be used to make a prediction.||In traditional programming, rule-based code is written by the developers depending on the problem statements.||Artificial Intelligence involves making the machine as much capable, So that it can perform the tasks that typically require human intelligence.|
|Machine Learning uses a data-driven approach, It is typically trained on historical data and then used to make predictions on new data.||Traditional programming is typically rule-based and deterministic. It hasn’t self-learning features like Machine Learning and AI.||AI can involve many different techniques, including Machine Learning and Deep Learning, as well as traditional rule-based programming.|
|ML can find patterns and insights in large datasets that might be difficult for humans to discover.||Traditional programming is totally dependent on the intelligence of developers. So, it has very limited capability.||Sometimes AI uses a combination of both Data and Pre-defined rules, which gives it a great edge in solving complex tasks with good accuracy which seem impossible to humans.|
|Machine Learning is the subset of AI. And Now it is used in various AI-based tasks like Chatbot Question answering, self-driven car., etc.||Traditional programming is often used to build applications and software systems that have specific functionality.||AI is a broad field that includes many different applications, including natural language processing, computer vision, and robotics.|
How machine learning algorithms work
Machine Learning works in the following manner.
- Forward Pass: In the Forward Pass, the machine learning algorithm takes in input data and produces an output. Depending on the model algorithm it computes the predictions.
- Loss Function: The loss function, also known as the error or cost function, is used to evaluate the accuracy of the predictions made by the model. The function compares the predicted output of the model to the actual output and calculates the difference between them. This difference is known as error or loss. The goal of the model is to minimize the error or loss function by adjusting its internal parameters.
- Model Optimization Process: The model optimization process is the iterative process of adjusting the internal parameters of the model to minimize the error or loss function. This is done using an optimization algorithm, such as gradient descent. The optimization algorithm calculates the gradient of the error function with respect to the model’s parameters and uses this information to adjust the parameters to reduce the error. The algorithm repeats this process until the error is minimized to a satisfactory level.
Once the model has been trained and optimized on the training data, it can be used to make predictions on new, unseen data. The accuracy of the model’s predictions can be evaluated using various performance metrics, such as accuracy, precision, recall, and F1-score.
Machine Learning lifecycle:
The lifecycle of a machine learning project involves a series of steps that include:
- Study the Problems: The first step is to study the problem. This step involves understanding the business problem and defining the objectives of the model.
- Data Collection: When the problem is well-defined, we can collect the relevant data required for the model. The data could come from various sources such as databases, APIs, or web scraping.
- Data Preparation: When our problem-related data is collected. then it is a good idea to check the data properly and make it in the desired format so that it can be used by the model to find the hidden patterns. This can be done in the following steps:
- Data cleaning
- Data Transformation
- Explanatory Data Analysis and Feature Engineering
- Split the dataset for training and testing.
- Model Selection: The next step is to select the appropriate machine learning algorithm that is suitable for our problem. This step requires knowledge of the strengths and weaknesses of different algorithms. Sometimes we use multiple models and compare their results and select the best model as per our requirements.
- Model building and Training: After selecting the algorithm, we have to build the model.
- In the case of traditional machine learning building mode is easy it is just a few hyperparameter tunings.
- In the case of deep learning, we have to define layer-wise architecture along with input and output size, number of nodes in each layer, loss function, gradient descent optimizer, etc.
- After that model is trained using the preprocessed dataset.
- Model Evaluation: Once the model is trained, it can be evaluated on the test dataset to determine its accuracy and performance using different techniques like classification report, F1 score, precision, recall, ROC Curve, Mean Square error, absolute error, etc.
- Model Tuning: Based on the evaluation results, the model may need to be tuned or optimized to improve its performance. This involves tweaking the hyperparameters of the model.
- Deployment: Once the model is trained and tuned, it can be deployed in a production environment to make predictions on new data. This step requires integrating the model into an existing software system or creating a new system for the model.
- Monitoring and Maintenance: Finally, it is essential to monitor the model’s performance in the production environment and perform maintenance tasks as required. This involves monitoring for data drift, retraining the model as needed, and updating the model as new data becomes available.
Types of Machine Learning
1. Supervised Machine Learning:
Supervised learning is a type of machine learning in which the algorithm is trained on the labeled dataset. It learns to map input features to targets based on labeled training data. In supervised learning, the algorithm is provided with input features and corresponding output labels, and it learns to generalize from this data to make predictions on new, unseen data.
There are two main types of supervised learning:
- Regression: Regression is a type of supervised learning where the algorithm learns to predict continuous values based on input features. The output labels in regression are continuous values, such as stock prices, and housing prices. The different regression algorithms in machine learning are: Linear Regression, Polynomial Regression, Ridge Regression, Decision Tree Regression, Random Forest Regression, Support Vector Regression, etc
- Classification: Classification is a type of supervised learning where the algorithm learns to assign input data to a specific category or class based on input features. The output labels in classification are discrete values. Classification algorithms can be binary, where the output is one of two possible classes, or multiclass, where the output can be one of several classes. The different Classification algorithms in machine learning are: Logistic Regression, Naive Bayes, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), etc
2. Unsupervised Machine Learning:
Unsupervised learning is a type of machine learning where the algorithm learns to recognize patterns in data without being explicitly trained using labeled examples. The goal of unsupervised learning is to discover the underlying structure or distribution in the data.
There are two main types of unsupervised learning:
- Clustering: Clustering algorithms group similar data points together based on their characteristics. The goal is to identify groups, or clusters, of data points that are similar to each other, while being distinct from other groups. Some popular clustering algorithms include K-means, Hierarchical clustering, and DBSCAN.
- Dimensionality reduction: Dimensionality reduction algorithms reduce the number of input variables in a dataset while preserving as much of the original information as possible. This is useful for reducing the complexity of a dataset and making it easier to visualize and analyze. Some popular dimensionality reduction algorithms include Principal Component Analysis (PCA), t-SNE, and Autoencoders.
3. Reinforcement Machine Learning
Reinforcement learning is a type of machine learning where an agent learns to interact with an environment by performing actions and receiving rewards or penalties based on its actions. The goal of reinforcement learning is to learn a policy, which is a mapping from states to actions, that maximizes the expected cumulative reward over time.
There are two main types of reinforcement learning:
- Model-based reinforcement learning: In model-based reinforcement learning, the agent learns a model of the environment, including the transition probabilities between states and the rewards associated with each state-action pair. The agent then uses this model to plan its actions in order to maximize its expected reward. Some popular model-based reinforcement learning algorithms include Value Iteration and Policy Iteration.
- Model-free reinforcement learning: In model-free reinforcement learning, the agent learns a policy directly from experience without explicitly building a model of the environment. The agent interacts with the environment and updates its policy based on the rewards it receives. Some popular model-free reinforcement learning algorithms include Q-Learning, SARSA, and Deep Reinforcement Learning.
Need for machine learning:
Machine learning is important because it allows computers to learn from data and improve their performance on specific tasks without being explicitly programmed. This ability to learn from data and adapt to new situations makes machine learning particularly useful for tasks that involve large amounts of data, complex decision-making, and dynamic environments.
Here are some specific areas where machine learning is being used:
- Predictive modeling: Machine learning can be used to build predictive models that can help businesses make better decisions. For example, machine learning can be used to predict which customers are most likely to buy a particular product, or which patients are most likely to develop a certain disease.
- Natural language processing: Machine learning is used to build systems that can understand and interpret human language. This is important for applications such as voice recognition, chatbots, and language translation.
- Computer vision: Machine learning is used to build systems that can recognize and interpret images and videos. This is important for applications such as self-driving cars, surveillance systems, and medical imaging.
- Fraud detection: Machine learning can be used to detect fraudulent behavior in financial transactions, online advertising, and other areas.
- Recommendation systems: Machine learning can be used to build recommendation systems that suggest products, services, or content to users based on their past behavior and preferences.
Overall, machine learning has become an essential tool for many businesses and industries, as it enables them to make better use of data, improve their decision-making processes, and deliver more personalized experiences to their customers.
Various Applications of Machine Learning
Now in this Machine learning tutorial, let’s learn the applications of Machine Learning:
- Automation: Machine learning, which works entirely autonomously in any field without the need for any human intervention. For example, robots perform the essential process steps in manufacturing plants.
- Finance Industry: Machine learning is growing in popularity in the finance industry. Banks are mainly using ML to find patterns inside the data but also to prevent fraud.
- Government organization: The government makes use of ML to manage public safety and utilities. Take the example of China with its massive face recognition. The government uses Artificial intelligence to prevent jaywalking.
- Healthcare industry: Healthcare was one of the first industries to use machine learning with image detection.
- Marketing: Broad use of AI is done in marketing thanks to abundant access to data. Before the age of mass data, researchers develop advanced mathematical tools like Bayesian analysis to estimate the value of a customer. With the boom of data, the marketing department relies on AI to optimize customer relationships and marketing campaigns.
- Retail industry: Machine learning is used in the retail industry to analyze customer behavior, predict demand, and manage inventory. It also helps retailers to personalize the shopping experience for each customer by recommending products based on their past purchases and preferences.
- Transportation: Machine learning is used in the transportation industry to optimize routes, reduce fuel consumption, and improve the overall efficiency of transportation systems. It also plays a role in autonomous vehicles, where ML algorithms are used to make decisions about navigation and safety.
Challenges and Limitations of Machine Learning-
Limitations of Machine Learning:
- The primary challenge of machine learning is the lack of data or the diversity in the dataset.
- A machine cannot learn if there is no data available. Besides, a dataset with a lack of diversity gives the machine a hard time.
- A machine needs to have heterogeneity to learn meaningful insight.
- It is rare that an algorithm can extract information when there are no or few variations.
- It is recommended to have at least 20 observations per group to help the machine learn. This constraint leads to poor evaluation and prediction.
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