Unveiling Basic Concepts of Machine Learning for Programmers

Machine learning is a field of artificial intelligence that enables machines to learn from data and improve their performance over time. It encompasses a set of algorithms and techniques that allow computers to autonomously analyze and interpret complex patterns and make predictions or decisions without explicit programming. For programmers, understanding the basic concepts of machine learning is essential in order to leverage its potential and develop innovative solutions.

In this article, we will walk you through the fundamental principles behind machine learning. We will explore the three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Each type has its own unique approach and applications, and gaining a solid understanding of these concepts is crucial to mastering machine learning.

Supervised learning involves training models using labeled data, where the desired output is known. It is commonly used for tasks like classification and regression, and programmers can leverage this technique to build predictive models that can accurately classify new data points.

On the other hand, unsupervised learning focuses on discovering patterns or groupings in unlabeled data, where the desired output is unknown. This type of learning is particularly useful for tasks like clustering and anomaly detection, and it can help programmers identify hidden insights within large datasets.

Reinforcement learning takes a different approach by training agents to make sequential decisions based on rewards or penalties. This type of learning is often used in scenarios where an agent must navigate and learn from its environment, such as in robotics or game playing. Programmers can utilize reinforcement learning to develop intelligent agents that optimize their behavior over time.

As you delve deeper into machine learning, you will discover its wide range of applications in various industries. Healthcare, finance, retail, natural language processing, and autonomous vehicles are just a few examples of sectors that are benefiting from the power of machine learning. By leveraging its capabilities, programmers can drive innovation and solve complex problems in these domains.

Choosing the right algorithm and technique is a critical step in a successful machine learning project. With a plethora of options available, programmers need to carefully consider factors such as the nature of the data, the task at hand, and computational resources. This article will provide guidance on selecting the most suitable approach for your specific needs.

Ethical considerations are also paramount in the field of machine learning. Bias, fairness, privacy, and interpretability are some of the ethical challenges that programmers must address when building machine learning models. We will explore these considerations and discuss strategies for ensuring ethical practices in your machine learning projects.

While machine learning offers tremendous opportunities, it also presents its own set of challenges. Data quality, overfitting, and scalability are just a few of the obstacles that programmers may encounter. By understanding these challenges and implementing effective strategies, you can overcome them and achieve successful outcomes.

The future of machine learning is exciting and filled with possibilities. Advancements in quantum machine learning and explainable AI are set to revolutionize the field, opening up new frontiers and expanding the scope of what can be accomplished. By staying up-to-date with the latest developments, programmers can stay ahead of the curve and be at the forefront of this transformative technology.

Embarking on your machine learning journey requires access to reliable resources and guidance. Whether you are a beginner or an experienced programmer, continuous learning is crucial to mastering machine learning. This article will provide you with valuable recommendations and online learning platforms like DropoutDeveloper, where you can find comprehensive courses and tutorials to enhance your skills.

Key Takeaways:

• Machine learning enables machines to learn from data and improve their performance over time.
• Supervised learning uses labeled data, while unsupervised learning deals with unlabeled data.
• Reinforcement learning trains agents to make decisions based on rewards or penalties.
• Machine learning has diverse applications in healthcare, finance, retail, and more.
• Choosing the right algorithm and considering ethical implications are crucial in machine learning projects.

The Three Main Types of Machine Learning

In machine learning, there are three main types that programmers need to understand and utilize: supervised learning, unsupervised learning, and reinforcement learning. Each type has its own unique approach and applications, playing a crucial role in solving real-world problems.

Supervised learning: This type of machine learning involves training a model using labeled data, where the input features and their corresponding output labels are provided. The goal is for the model to learn the mapping between the input and output, allowing it to make predictions or classifications on new and unseen data. Supervised learning algorithms include decision trees, support vector machines, and neural networks.

Unsupervised learning: Unlike supervised learning, unsupervised learning deals with unlabeled data, where only the input features are provided. The objective is to discover patterns, relationships, or groupings within the data without explicit guidance or labels. Unsupervised learning algorithms include clustering algorithms such as k-means and hierarchical clustering, as well as dimensionality reduction techniques like principal component analysis.

Reinforcement learning: Reinforcement learning is a type of machine learning that focuses on training an agent to make sequential decisions based on rewards or penalties. The agent learns through trial and error by interacting with an environment and receiving feedback in the form of rewards or punishments. Reinforcement learning algorithms aim to maximize the long-term reward by finding the optimal policy or strategy. This type of learning is often used in robotics, game playing, and autonomous systems.

Type	Input	Output	Examples
Supervised learning	Labeled data	Predictions or classifications	Decision trees, support vector machines, neural networks
Unsupervised learning	Unlabeled data	Patterns or groupings	K-means clustering, hierarchical clustering, principal component analysis
Reinforcement learning	Sequential decisions, rewards or penalties	Optimal policy or strategy	Q-learning, deep Q networks

Supervised Learning: Training Models with Labeled Data

Supervised learning is a key technique in machine learning, where programmers train models using labeled data to make accurate predictions or classifications. This type of learning involves providing input data and the corresponding correct output labels to the algorithm, allowing it to learn from the provided examples. The goal is for the model to generalize from the labeled data and accurately predict the output for new, unseen data.

One of the advantages of supervised learning is that it allows programmers to solve a wide range of problems, including image classification, natural language processing, and even predicting stock prices. By feeding the algorithm with labeled data, programmers can guide the model to learn patterns and relationships, enabling it to make informed predictions or classifications with high accuracy.

During the training process, the algorithm learns to identify patterns and correlations in the data, adjusting its internal parameters to minimize the difference between the predicted output and the actual labeled data. This process is often referred to as model optimization or training, and it involves using various techniques such as gradient descent to find the optimal values for the model’s parameters.

Benefits of Supervised Learning:

• Accurate predictions or classifications based on labeled data.
• Ability to solve a wide range of problems in different domains.
• Capability to handle both numeric and categorical data.
• Opportunity to continuously improve model performance through iterative training and optimization.

Overall, supervised learning is an essential tool for programmers in the field of machine learning. By understanding and implementing supervised learning algorithms, programmers can leverage labeled data to build accurate models that can make predictions or classifications with high precision.

Supervised Learning	Unsupervised Learning
Training models using labeled data	Finding patterns in unlabeled data
Accurate predictions or classifications	Grouping similar data points
Wide range of applications	Data exploration and clustering

Unsupervised Learning: Discovering Patterns in Unlabeled Data

Unsupervised learning allows programmers to explore and discover hidden patterns or groupings in unlabeled data, without the need for predefined labels. It is a powerful technique in machine learning that can reveal valuable insights and trends from vast amounts of unstructured information.

One of the primary goals of unsupervised learning is to cluster data points into meaningful groups based on their intrinsic similarities. This enables programmers to identify patterns, relationships, or anomalies that may not be immediately apparent. Examples of unsupervised learning algorithms include k-means clustering, hierarchical clustering, and principal component analysis (PCA).

By applying unsupervised learning techniques, programmers can gain a deeper understanding of complex datasets and make data-driven decisions. For instance, in the healthcare industry, unsupervised learning can assist in patient segmentation, identifying subgroups with similar medical characteristics for personalized treatments. In finance, it can be used for fraud detection by grouping suspicious transactions based on their patterns and behaviors.

Benefits of Unsupervised Learning	Applications
Reveals hidden patterns and relationships Identifies anomalies or outliers Reduces dimensionality for easier interpretation	Customer segmentation and targeting Anomaly detection and fraud detection Data pre-processing and feature extraction

Unsupervised learning plays a crucial role in various real-world applications, including recommendation systems, image and speech recognition, natural language processing, and market research. It empowers programmers to extract valuable information from unstructured data and gain actionable insights to drive decision-making and innovation.

Reinforcement Learning: Training Agents Based on Rewards

Reinforcement learning enables programmers to train agents to make sequential decisions based on rewards or penalties, learning from feedback to optimize their actions. This type of machine learning is particularly useful in scenarios where an agent interacts with an environment, taking actions to maximize a reward signal over time.

Through reinforcement learning, programmers can develop algorithms that allow agents to learn from experience and improve their decision-making abilities. The agents receive feedback in the form of rewards or penalties, which guide them towards actions that yield positive outcomes and away from actions that lead to negative outcomes. By iteratively adjusting their strategies based on this feedback, the agents can learn to make optimal decisions in complex and dynamic environments.

Reinforcement learning algorithms often employ the use of a policy, which is a rule or strategy that guides the agent’s actions. The policy can be updated through various methods, such as Q-learning or Monte Carlo methods. These algorithms enable the agent to explore different actions, learn from their outcomes, and gradually converge on the most rewarding strategies.

Advantages of Reinforcement Learning	Challenges in Reinforcement Learning
Ability to handle sequential decision-making problems Adaptability to dynamic and uncertain environments Potential for learning optimal strategies over time	Complexity in defining reward structures Issue of balancing exploration and exploitation High computational requirements

Reinforcement learning offers a powerful framework for training agents to learn from feedback and optimize their actions. By leveraging rewards or penalties, programmers can guide agents towards making decisions that yield positive outcomes. While it presents challenges such as defining reward structures and balancing exploration and exploitation, reinforcement learning holds immense potential in solving complex sequential decision-making problems.

Applications of Reinforcement Learning

Reinforcement learning finds applications in various domains, including robotics, game playing, recommendation systems, and autonomous vehicles. For example, reinforcement learning algorithms can be used to train robots to perform complex tasks by learning from trial and error. In game playing, reinforcement learning has been utilized to develop AI agents that can compete against human players or solve challenging game scenarios.

Furthermore, reinforcement learning techniques have been employed in recommendation systems to personalize and optimize content recommendations for users. By learning from user feedback and interaction patterns, these systems can enhance the user experience and improve the effectiveness of recommendations.

In the field of autonomous vehicles, reinforcement learning plays a crucial role in training self-driving cars to make safe and efficient driving decisions. Agents can learn to navigate complex traffic scenarios and adapt to changing road conditions, improving overall road safety and traffic management.

Summary

Reinforcement learning is a type of machine learning that enables programmers to train agents to make sequential decisions based on rewards or penalties. It enables agents to learn from experience and optimize their actions over time. While it presents challenges such as defining reward structures and balancing exploration and exploitation, reinforcement learning has various applications in domains such as robotics, game playing, recommendation systems, and autonomous vehicles. By harnessing the power of reinforcement learning, programmers can develop intelligent agents capable of navigating complex environments and making optimal decisions.

Advantages of Reinforcement Learning	Challenges in Reinforcement Learning
Ability to handle sequential decision-making problems Adaptability to dynamic and uncertain environments Potential for learning optimal strategies over time	Complexity in defining reward structures Issue of balancing exploration and exploitation High computational requirements

Applications of Machine Learning in Various Industries

Machine learning has extensive applications across various industries, revolutionizing sectors like healthcare, finance, retail, natural language processing, and autonomous vehicles. In healthcare, machine learning algorithms are used to analyze medical images, predict disease outcomes, and personalize treatments. This technology has the potential to improve diagnostics accuracy and enhance patient care by enabling early detection of diseases.

In finance, machine learning is employed for fraud detection, risk assessment, and algorithmic trading. By analyzing large volumes of financial data, machine learning models can identify patterns and anomalies to detect fraudulent activities and automate trading decisions, leading to more efficient and secure financial transactions.

Retail industry benefits from machine learning algorithms to optimize pricing strategies, forecast demand, and enhance customer experience. By analyzing consumer behavior and historical data, machine learning models can suggest personalized product recommendations, improve inventory management, and enhance customer satisfaction.

Natural Language Processing (NLP) is another area where machine learning plays a crucial role. NLP algorithms enable machines to understand and interpret human language, leading to advancements in voice recognition, sentiment analysis, and chatbots. NLP technologies are used in virtual assistants, customer support systems, and language translation services.

Table 1: Examples of Machine Learning Applications

Industry	Machine Learning Application
Healthcare	Medical image analysis, disease prediction, personalized treatment
Finance	Fraud detection, risk assessment, algorithmic trading
Retail	Pricing optimization, demand forecasting, personalized recommendations
Natural Language Processing	Voice recognition, sentiment analysis, chatbots

Autonomous vehicles are also benefiting from machine learning algorithms, enabling them to navigate and respond to complex traffic situations. Machine learning models can learn from vast amounts of sensor data to improve driving performance, enhance safety, and revolutionize the transportation industry.

As machine learning continues to advance, its applications are expected to expand further, transforming industries and revolutionizing the way we live and work.

Choosing the Right Algorithm and Technique

Selecting the appropriate algorithm and technique is crucial for successful machine learning implementations. Programmers must understand the factors and trade-offs involved in choosing the right approach. There are several considerations to keep in mind when deciding on the best algorithm and technique for a specific machine learning task.

Consider the Nature of the Data

The first step in selecting the right algorithm and technique is analyzing the nature of the data. Is it structured or unstructured? Does it contain numerical or categorical variables? Understanding the data’s characteristics will help determine which machine learning approach is most suitable.

For structured data with labeled outputs, supervised learning algorithms like linear regression or decision trees can be used. On the other hand, if the data is unlabeled, unsupervised learning algorithms such as clustering or dimensionality reduction techniques may be more appropriate.

Evaluate Model Accuracy

Another crucial factor to consider is the desired level of accuracy. Different algorithms and techniques have varying performance levels and trade-offs. Some algorithms may provide higher accuracy but require more computational resources, while others may have lower accuracy but are faster and more efficient.

It is important to assess the trade-offs between accuracy, training time, and computational requirements. This evaluation process will help determine which algorithm and technique strike the right balance for the specific requirements of the machine learning task.

Understand the Model’s Interpretability

Interpretability is increasingly becoming an important consideration in machine learning. Some industries, such as healthcare or finance, require models that can provide explanations for their predictions or decisions. In these cases, it is crucial to select algorithms and techniques that offer interpretability.

Linear regression or decision tree algorithms are often preferred in such scenarios due to their transparency and ability to provide insights into the model’s decision-making process. However, it is important to note that more complex algorithms, like deep learning models, may provide higher accuracy but lack interpretability.

Consideration	Algorithm and Technique
Data Nature	Structured with labeled outputs: Supervised Learning Algorithms (Linear Regression, Decision Trees) Unlabeled: Unsupervised Learning Algorithms (Clustering, Dimensionality Reduction)
Model Accuracy	Trade-offs between accuracy, training time, and computational requirements
Interpretability	Linear Regression, Decision Trees for transparency and insights into the decision-making process

By considering the nature of the data, evaluating model accuracy, and understanding interpretability requirements, programmers can make informed decisions when selecting the most suitable algorithm and technique for their machine learning projects. This knowledge is essential for successful implementations and ensuring the best possible outcomes.

Ethical Considerations in Machine Learning

As machine learning becomes more pervasive, programmers must navigate ethical considerations, including bias and model interpretability, to ensure fair and responsible use of these technologies. When developing machine learning models, it is crucial to be aware of the potential biases that could be introduced through the data or algorithm. Bias can lead to unfair outcomes or discrimination against certain groups, perpetuating societal inequalities. Therefore, it is essential to thoroughly evaluate and address bias in both the data used for training and the algorithm employed.

Model interpretability is another ethical consideration in machine learning. As models become increasingly complex, it can be challenging to understand and interpret their decision-making processes. This lack of transparency may raise concerns in various domains, such as healthcare or finance, where the decisions made by machine learning algorithms can significantly impact individuals’ lives. Ensuring model interpretability allows stakeholders to understand why and how a model arrived at a particular decision, fostering trust and enabling accountability.

Addressing Bias and Model Interpretability

To address bias in machine learning, programmers should implement strategies such as data preprocessing, diverse input data, and algorithmic fairness techniques. Data preprocessing involves thoroughly examining and cleaning the data to identify and mitigate any biases present. Additionally, using diverse and representative data can help reduce bias by ensuring that the training data includes a broad range of examples from different demographic groups.

For model interpretability, techniques such as feature importance analysis, surrogate models, and post-hoc explanation methods can be applied. Feature importance analysis helps identify which input features most heavily influence a model’s decisions. Surrogate models provide a simplified representation of complex models, making them easier to interpret. Post-hoc explanations, such as generating textual or graphical explanations for individual predictions, can also enhance model interpretability.

In conclusion, ethical considerations are paramount in machine learning to foster fairness, transparency, and accountability. By addressing bias and ensuring model interpretability, programmers can contribute to the responsible and ethical development and deployment of machine learning systems. By prioritizing these considerations, we can build a future where machine learning benefits society as a whole.

Challenges in Machine Learning

Machine learning projects present various challenges, including data quality, overfitting, and scalability. Programmers need to be aware of these challenges and adopt effective strategies to overcome them. Let’s take a closer look at each challenge:

Data Quality:

High-quality data is crucial for successful machine learning projects. However, real-world data can often be messy, incomplete, or contain outliers. Programmers need to carefully preprocess and clean the data to ensure its accuracy and reliability. This may involve handling missing values, removing outliers, and dealing with inconsistencies. Additionally, data imbalance can also pose challenges, especially in binary classification problems where one class dominates the dataset. Programmers can address this by employing techniques such as oversampling or undersampling to create a balanced training dataset.

Overfitting:

Overfitting occurs when a model becomes too complex and starts to perform well on the training data but poorly on new, unseen data. This phenomenon can be a major challenge in machine learning. To overcome overfitting, programmers can employ techniques such as regularization, cross-validation, and early stopping. Regularization methods, such as L1 and L2 regularization, help prevent the model from becoming too complex by adding a penalty term to the loss function. Cross-validation helps estimate the model’s performance on unseen data, allowing programmers to detect and mitigate overfitting. Early stopping, on the other hand, stops the training process when the model’s performance on a validation set starts to deteriorate, preventing unnecessary training.

Scalability:

Scalability is another challenge in machine learning, especially when dealing with large datasets or computationally intensive models. As the size of the dataset or complexity of the model increases, the training process can become slow or even unfeasible. Programmers can address this challenge by employing techniques such as dimensionality reduction, parallel computing, and distributed computing. Dimensionality reduction techniques, such as Principal Component Analysis (PCA) or t-SNE, can reduce the number of features and simplify the dataset without losing important information. Parallel computing, using multiple processors or GPUs, can accelerate the training process by dividing the workload. Distributed computing, leveraging multiple machines, can further enhance scalability by distributing the data and computation.

By understanding and addressing these challenges, programmers can enhance the success of their machine learning projects. To optimize performance and ensure accurate results, it is essential to preprocess and clean data, prevent overfitting, and design scalable solutions. Overcoming these challenges contributes to the development of robust and effective machine learning models.

Challenge	Recommendations
Data Quality	Preprocess and clean the data Handle missing values Remove outliers Address data imbalance
Overfitting	Apply regularization techniques Employ cross-validation Utilize early stopping
Scalability	Use dimensionality reduction techniques Leverage parallel computing Explore distributed computing

The Future of Machine Learning

The future of machine learning holds exciting developments, such as quantum machine learning and explainable AI, which have the potential to revolutionize the field and expand its capabilities. Quantum machine learning combines the power of quantum computing with machine learning algorithms, enabling the processing and analysis of vast amounts of data at unprecedented speeds. This breakthrough technology has the potential to solve complex problems in various industries, such as drug discovery and optimization of complex systems.

Explainable AI, on the other hand, focuses on making machine learning models more transparent and interpretable. It aims to address the black box nature of some algorithms, allowing users to understand how and why certain decisions are made. This is crucial in critical applications such as healthcare and finance, where transparency and accountability are necessary.

These advancements in machine learning will open up new possibilities and opportunities for programmers. With quantum machine learning, complex problems that were once deemed unsolvable may become solvable in a fraction of the time. Explainable AI will enable programmers to build more reliable and trustworthy machine learning models, fostering greater trust and acceptance of AI technology in society.

As the field of machine learning continues to evolve, it is imperative for programmers to stay updated with the latest developments and trends. Continuous learning and exploration of new techniques and algorithms will be key to unlocking the full potential of machine learning and driving innovation in various industries.

Advancements in Machine Learning	Potential Impact
Quantum Machine Learning	– Faster data processing and analysis – Solving complex problems in various industries
Explainable AI	– Transparent and interpretable machine learning models – Greater trust and acceptance of AI technology

Resources and Guidance for Starting the Machine Learning Journey

Embarking on the machine learning journey requires access to resources and guidance. We recommend online learning platforms like DropoutDeveloper, which provide a wealth of free coding resources for self-taught programmers. Whether you’re a beginner looking to grasp the fundamentals or an experienced programmer aiming to enhance your machine learning skills, DropoutDeveloper offers a comprehensive collection of tutorials, articles, and interactive exercises to cater to your learning needs.

One of the key advantages of DropoutDeveloper is its beginner-friendly approach. The platform offers an introduction to machine learning for programmers at all levels of experience. You can start with basic concepts like supervised learning, unsupervised learning, and reinforcement learning and gradually progress to advanced topics like deep learning and natural language processing. The step-by-step tutorials and real-world examples ensure a practical understanding of the concepts.

In addition to the extensive learning materials, DropoutDeveloper provides a supportive community of fellow learners and industry experts. You can engage in discussions, seek guidance, and collaborate on projects with like-minded individuals. The platform also offers coding challenges and competitions to test your skills and boost your learning curve.

By choosing DropoutDeveloper as your online learning platform, you gain access to a comprehensive ecosystem that empowers you with the knowledge and skills to excel in the field of machine learning. Start your journey today and unlock the endless possibilities that machine learning has to offer.

Why Choose DropoutDeveloper?	Key Features
Comprehensive Learning Materials	– Tutorials and articles – Interactive exercises – Real-world examples
Supportive Community	– Engage in discussions – Seek guidance – Collaborate on projects
Coding Challenges and Competitions	– Test your skills – Boost your learning curve
Beginner-Friendly Approach	– Introduction to basic concepts – Practical understanding of advanced topics

About DropoutDeveloper – Your Online Learning Platform

DropoutDeveloper is an online learning platform that offers a treasure trove of free coding resources, tailored to meet the needs of self-taught programmers aiming to improve their skills in web development and other IT domains. With our comprehensive collection of tutorials, articles, and video lectures, we provide a structured learning path that covers programming basics of machine learning and beyond.

Aspiring programmers can take advantage of our vast library of content, covering a wide range of topics such as HTML, CSS, JavaScript, Python, and more. Whether you’re a beginner or an experienced developer looking to expand your knowledge, DropoutDeveloper is here to support your learning journey.

Our platform offers interactive coding exercises, quizzes, and projects to reinforce your understanding and ensure practical application of the concepts. You can also join our vibrant community of learners and professionals, where you can exchange ideas, collaborate on projects, and seek guidance from experienced mentors.

At DropoutDeveloper, we believe that continuous learning is crucial in today’s fast-paced tech industry. That’s why we strive to provide quality educational resources that empower self-taught programmers to stay up-to-date with the latest technologies and enhance their employment prospects. Start your machine learning journey with us today and unlock a world of possibilities!

Table: Features of DropoutDeveloper

Features	Benefits
Free coding resources	Access a wealth of materials without any cost
Structured learning path	Follow a step-by-step curriculum designed to build your skills
Interactive exercises	Apply your knowledge through hands-on coding practice
Community support	Connect with fellow learners and experienced mentors
Stay up-to-date	Keep pace with the latest technologies and industry trends

Conclusion

Understanding the basic concepts of machine learning is crucial for programmers seeking to leverage this powerful technology to solve real-world problems and drive innovation. Machine learning is a field of artificial intelligence that enables machines to learn from data and improve their performance over time. By comprehending the three main types of machine learning, namely supervised learning, unsupervised learning, and reinforcement learning, programmers can apply the appropriate techniques to their projects.

In supervised learning, programmers train models using labeled data to make predictions or classifications. On the other hand, unsupervised learning allows programmers to discover patterns or groupings in unlabeled data, enabling valuable insights and decision-making. Reinforcement learning takes a different approach, training agents to make sequential decisions by rewarding or penalizing their actions.

Machine learning finds applications in various industries, such as healthcare, finance, retail, natural language processing, and autonomous vehicles. Programmers will benefit from understanding the wide-ranging algorithms and techniques used in machine learning and choosing the right approach for their specific tasks. It’s crucial to consider ethical considerations in machine learning development, addressing issues like bias and model interpretability.

The future of machine learning holds exciting developments, including quantum machine learning and explainable AI, which promise to revolutionize the field even further. As programmers embark on their machine learning journey, they can rely on valuable resources and guidance to learn the necessary concepts and techniques. DropoutDeveloper, an online learning platform, offers free coding resources and expert guidance to help programmers enhance their skills in machine learning and various IT domains.

Source Links

• https://medium.com/@sonalika_07/introduction-to-machine-learning-unveiling-the-power-of-smart-algorithms-4b01caa41947
• https://medium.com/@coolmister456/unveiling-the-magic-of-machine-learning-a-journey-into-the-world-of-algorithms-b56e9d36cbaa
• https://www.techtarget.com/searchenterpriseai/definition/machine-learning-ML