Data Science & Machine Learning

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1. What is DBSCAN Clustering? DBSCAN groups ‘densely grouped’ data points into a single cluster. It can identify clusters in large spatial datasets by looking at the local density of the data points. The most exciting feature of DBSCAN clustering is that it is robust to outliers. It also does not require the number of clusters to be told beforehand, unlike K-Means, where we have to specify the number of centroids. 2. What are the different forms of joins in a table? SQL has many kinds of different joins including INNER JOIN, SELF JOIN, CROSS JOIN, and OUTER JOIN. In fact, each join type defines the way two tables are related in a query. OUTER JOINS can further be divided into LEFT OUTER JOINS, RIGHT OUTER JOINS, and FULL OUTER JOINS. 3.How is the grid search parameter different from the random search? Model Hyperparameter tuning is very useful to enhance the performance of a machine learning model. The only difference between both the approaches is in grid search we define the combinations and do training of the model whereas in RandomizedSearchCV the model selects the combinations randomly. Both are very effective ways of tuning the parameters that increase the model generalizability. Random search is a technique where random combinations of the hyperparameters are used to find the best solution for the built model. The drawback of random search is that it yields high variance during computing. Since the selection of parameters is completely random; and since no intelligence is used to sample these combinations, luck plays its role. 4.How should you maintain a deployed model? A deployed model needs to be retrained after a while so as to improve the performance of the model. Since deployment, a track should be kept of the predictions made by the model and the truth values. Later this can be used to retrain the model with the new data. Also, root cause analysis for wrong predictions should be done.

Join us this week in the FREE Webinars and First Lessons and in a short 2–4 months you’ll have a well-paying job in tech — no prior experience required! Get ready to launch 🚀 your tech career: our training includes interactive classes, Job Application Service (JAS), and a built — in internship that will give you the skills and experience you need to succeed. Plus, your 1:1 career mentor will prep you for your job interviews so you’ll land a job fast ▶️ January 24  - Tech Jobs for Beginners: Become a Software Tester. Free Webinar ▶️ January 25  - Fast Track  to Level-Up your Tech Career 2023: QA Automation. Free Webinar ▶️ January 26  - Tech Jobs for Beginners: Become a Software Tester. Free Webinar ▶️ January 23 - Sales Engineering. First Free Lesson ▶️ January 26 -Manual QA. First Free Lesson ▶️ January 26 - UX Design. First Free Lesson▶️ January 31 - Tech Support. First Free Lesson Special offer for all participants! ️✅ Apply by the link

How to start learning Data Science? There are many resources available to help you start learning data science, depending on your background and goals. Here are a few steps you can take: Develop a strong understanding of the basics of statistics and programming. Learn Python or R programming languages, both are popular among data scientists. Learn the basics of data manipulation and visualization with tools such as pandas and matplotlib. Learn the basics of machine learning, such as linear regression and k-nearest neighbors, and practice applying them to real-world datasets. Take online courses and tutorials, such as those offered by Coursera, edX, and DataCamp. Practice by working on projects and participating in online data science competitions. Get familiar with popular data science libraries such as numpy, scikit-learn, tensorflow, keras and pytorch. It's a good idea to start with a solid foundation in statistics and programming, and then build on that foundation by learning the specific tools and techniques used in data science. As you gain experience, you can start working on more complex projects and exploring specialized areas of the field.

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