Machine Learning Programmer

  Machine Learning (ML) is a subset of Artificial Intelligence. It is the technology that imparts machines the ability to learn independently and improve their performance without being explicitly programmed. This science helps in building applications that learn from data over time with minimum or no human intervention. Proper Machine Learning training under experienced tutors in the industry will guide you in various areas such as applying ML algorithms, best practices for utilizing libraries and tools, etc., to solve real-world problems. So, what does a Machine Learning developer do? An ML developer performs the following steps while executing a project: Data collection: The first step in any ML project is data collection; you collect the data to be fed into the Machine Learning model. Programmers need to ensure that the data is of a prime quality since data quality directly affects the outcome and predictions. Data preparation: This is the second step. It involves distributing the

  Machine learning is all about training machines by feeding data to algorithms. But this becomes a challenging task as the data needs to be error-free before feeding to the machines. Therefore, cleaning the erroneous and irrelevant data is crucial for achieving efficiency and accuracy in results. While utilizing the ML data, the most tedious and time-consuming task is the cleaning of data. Inaccurate and irrelevant data can affect the quality of the training data for analytics. Data analysts and scientists have to spend an enormous amount of time classifying erroneous data. They do this through qualitative and quantitative techniques. The qualitative method includes patterns, constraints, and rules, while the quantitative method uses statistics to identify errors. Usually, data cleaning involves two steps, first identifying the error and, secondly, solving it. When it comes to data cleaning, there are certain practices that most data scientists use. So, consider using the following po