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How Machine Learning Algorithms Can Be Used for data science assignment

Skills required for Machine Learning:In data science, machine learning is a subfield of computer science concerned with the development of algorithms that allow computers to 'learn'. Data scientists work on a product or company that interacts with a large number of stakeholders and users in their daily work. However, they lack the means to update the system or tools based on recent customer feedback or behavior (on how well their software works, whether it meets user expectations, and what features should be added).

Machine learning can help! Data scientists can now build tools (machine learning models) that learn from existing data, such as logs or databases about software usage. These tools can make predictions based on new records, such as incoming requests, for example, analyze the content of an email and predict whether it belongs in the spam folder or not.

 A very practical example is using machine learning techniques to classify the sentiment of tweets. It can be used to measure customer satisfaction with your product or how well your marketing campaign went and whether you tweeted enough.

 Typically, data scientists need to understand human language and experience writing rules that can automatically categorize these types of sentiments, but machine learning comes into play and automates and improves these processes. You can start with a simple solution, such as an algorithm that uses certain keywords/expressions that are classified as positive/negative sentiment in tweets, but iteratively improves by adding new patterns (keywords) and correcting them based on what the data scientists know.

What is Machine Learning:

It's about understanding and uncovering hidden patterns or insights from data, which helps make smarter business decisions.

It is a subfield of data science that allows machines to learn automatically from previous data and experiences.

It is used to find business insights/information from raw data with the help of data analytics methods.

It is used to make predictions for new data points and to rank the results.

It is a broad term that includes the various steps involved in building a model for a given problem and applying the model in the real time production environment

It is used in the data modeling step of data science as a complete process. Machine learning can be applied on raw, structured or unstructured data.

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Skills required for Machine Learning:

A data scientist must have skills in the use of big data tools such as Hadoop, Hive and Pig, statistics, programming in Python, R/Shiny, R/Rstudio,  SAS or Scala.

A machine learning engineer must have skills like computer science fundamentals, programming skills in Python or R, concepts of statistics and probability, etc.

Data scientists spend a lot of time manipulating data, cleaning it, and understanding its patterns. ML engineers spend a lot of time managing the complexities that arise during the implementation of algorithms and the mathematical concepts behind them.