Machine Learning Life Cycle

Summary:

The machine learning life cycle is a structured, seven-step process to develop an effective machine learning project. The life cycle begins with Gathering Data, which focuses on identifying various data sources and collecting quality data that can help in predictive modelling. After gathering, Data Preparation is done to randomize and structure the data, which is further explored and pre-processed for characteristics and quality.

The third step, Data Wrangling, involves cleaning and converting the collected data into a usable format, addressing missing values, duplicate data, and noise. Following wrangling, Data Analysis is performed, where analytical techniques are chosen, and machine learning models are built to interpret the data. Algorithms like Classification, Regression, and Cluster analysis may be applied during this stage.

The fifth stage is Training the model, utilizing various machine learning algorithms to improve its performance by understanding patterns, rules, and features. Once trained, the model undergoes Testing to evaluate its accuracy and reliability. Finally, Deployment happens; the model is integrated into real-world systems after ensuring it meets project requirements and performs well on new data.

Continuous evaluation and iterations may occur throughout these seven steps to fine-tune the model for optimal results. The life cycle is essential not just for structuring the machine learning project but also for solving the specific problem at hand, ensuring both the efficiency and efficacy of the solution.

Excerpt:

Machine Learning Life Cycle

Machine learning has allowed computer systems to automatically learn without being explicitly programmed. But how does a machine learning system work? So, it can be described using the life cycle of machine learning. The machine learning life cycle is a cyclic process to build an efficient machine learning project. The main purpose of the life cycle is to find a solution to the problem or project.

The machine learning life cycle involves seven major steps, which are given below:

• Gathering Data
• Data preparation
• Data Wrangling
• Analyse Data
• Train the model
• Test the model
• Deployment