{"id":56984,"date":"2019-05-28T09:32:00","date_gmt":"2019-05-28T04:02:00","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=56984"},"modified":"2025-08-02T20:03:55","modified_gmt":"2025-08-02T14:33:55","slug":"machine-learning-for-data-science","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/machine-learning-for-data-science\/","title":{"rendered":"Want a Thriving Career? Focus on Machine Learning for Data Science!"},"content":{"rendered":"

Machine Learning is one of those fields that has taken over the world by storm<\/strong>. Aligned with Data Science, it has given a new meaning to the way we define and perceive data. In this article, we will see how relevant machine learning is in data science.<\/span><\/p>\n

Furthermore, we will discuss various machine learning algorithms and also the tools used for machine learning.<\/span><\/p>\n

Data Science is a vast domain of various underlying data operations. Predictive Modeling is one of the final steps of Data Science. In order to fully incorporate the power of predictive analytics, we make use of Machine Learning.<\/p>\n

This field has been around for many years but has recently gained popularity due to the emergence of Deep Learning <\/strong>and high-performance computing.<\/span><\/p>\n

At the end of this article, I will provide you the best Machine Learning Tutorials Series designed by DataFlair that will help you to master Machine Learning for Data Science. Let’s start-\u00a0<\/span><\/p>\n

Machine Learning for Data Science <\/span><\/h3>\n

Machine Learning is one of the most important processes in Data Science. With the help of machine learning, you can develop models that identify patterns in data and produce predictions. Machine Learning extends the procedure of Data Science beyond its scope.<\/span><\/p>\n

While a Data Scientist need not have knowledge of all the algorithms of Machine Learning, he\/she must have at least the knowledge of necessary algorithms that appeal to the domain usage. <\/span><\/p>\n

For example<\/strong>, data scientists working in the finance industry must have knowledge of regression and time-series machine learning algorithms that will help them to predict the movement of stock prices. <\/span><\/p>\n

Data Scientist in healthcare<\/strong> may also require knowledge of clustering and classification algorithms. However, in order to have a well-versed insight into Machine Learning, considerable knowledge of almost every important algorithm is required. <\/span><\/p>\n

Formal definition of Machine Learning<\/span><\/h3>\n

Machine Learning is a branch of computer science and statistics where computers can perform tasks without an explicit requirement for programming. The model on which\u00a0machine learning algorithms<\/strong> build is based on the training data which is a collection of historical datasets. <\/span><\/p>\n

After training the algorithm over several instances of data, it is then tested or validated against new instances of data called test-data. Finally, the results are taken, accuracy is measured and the performance of the model is tuned to give out better results. <\/span><\/p>\n

Machine Learning Algorithms can be classified into two types – <\/span><\/p>\n

    \n
  1. Supervised Learning<\/span><\/li>\n
  2. Unsupervised Learning<\/span><\/li>\n<\/ol>\n

    Supervised Learning<\/span><\/h3>\n

    1. Simple Linear Regression<\/h4>\n

    With the help of Linear Regression, we can measure the relationship between the two variables that are continuous. These two variables are <\/span><\/p>\n