Supervised learning algorithms are a type of machine learning algorithms that learn from labeled training data to predict outcomes for unseen or future data. These algorithms are constructed around a target function which is further refined through iterative learning until optimal performance is achieved.

Supervised learning algorithms can be categorized into two types: regression and classification algorithms. Regression algorithms are used to predict continuous outcomes, such as the stock market trends, while classification algorithms are used to predict discrete outcomes, such as categorizing emails as spam or not-spam.

• Structured Data: Supervised learning algorithms require organized and labeled data to train effectively. The better the data quality and labeling, the more accurately the algorithm can predict outcomes.
• Guided Learning: The algorithm uses the input-output pairs in the training data to learn the mapping function. With every correct prediction, the algorithm improves, and every incorrect prediction leads it to adjust the weight of the features.
• Real-world Applications: Supervised learning algorithms have become the bedrock of many practical applications - from recommendation engines to medical diagnosis and credit scoring models.
• Predictive Accuracy: The primary measure of success for supervised learning algorithms is prediction accuracy. Their magnitude of learning is often gauged based on the least number of errors in predicting outcomes.
• Vendor Support: Various technology vendors and communities offer tools, libraries and support for Implementation of Supervised algorithms, ensuring smooth operations.
• Cost-Effectiveness: Comparatively, Supervised Learning Algorithms proves to be more cost-effective than manual analysis. They provide productive insights and predictions for complex data patterns.

Implementing supervised learning algorithms requires careful planning, a solid understanding of the data and its underlying patterns, and meticulous testing.

Initially, the process involves gathering and cleaning data, followed by feature extraction. Training the algorithm with labeled data comes next, followed by testing it with unseen data. Iterative improvements are then made until the algorithm meets the desired level of accuracy and generalisation.

It must be recognized that deployment and monitoring are critical post-implementation steps to ensure the algorithm continues to perform optimally and adapt to new patterns in data.

Harnessing the power of supervised learning algorithms can unlock a wealth of opportunities for numerous businesses and industries. However, to ensure its successful implementation, a clear understanding of the objectives and the limitations are necessary.

These algorithms have several distinct advantages:

• Predictive Power: Because supervised learning algorithms learn from data with known outcomes, they are incredibly accurate in predicting outcomes for unseen data.
• Real-Time Analysis: Supervised learning algorithms is effective in making real-time decisions and predictions from real-time data.
• Hands-on Control: Supervised learning allows for more control over the learning process due to its need for labelled data allowing greater accuracy and reliability.
• Wide Applicability: The practical use-cases for these algorithms abound in virtually every major industry, from healthcare and finance to retail and transportation.
• Standardized Workflows: Supervised learning algorithms often follow standardized workflows, making it easier to integrate them into existing systems and processes.

While these advantages make supervised learning algorithms attractive, it's important to note some limitations.

These include:

• Availability of Labeled Data: One of the significant drawbacks is the need for labeled data. Without accurate labeling, the algorithm will not learn effectively.
• Overfitting: Supervised learning algorithms often tend to memorize training data sets, leading to overfitting. The algorithms might perform well on training data but fail to generalize well on unseen data.
• Time-consuming:The process of training the model on large data sets is often time-consuming.
• Dependency: Businesses may find themselves heavily reliant on data scientists for maintaining and updating the machine learning models.

Despite these limitations, supervised learning algorithms can be highly advantageous with the right implementation approach.