Future Data Science(FDS)

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#LinearRegressionChallenge Let x be 1, 2, 3 y be 5, 1, 3 m be 8 b be 40 and N is the total number of Dataset we have Note: N is the number of points, i.e. the length of the x list or the y list. find -2/N(Summation of (yi -(mxi+b) if i starts from 1 to N Use python code post you solution @pythonEthbot

What is Scikit-learn? Scikit-learn is a library in Python that provides many unsupervised and supervised learning algorithms. It’s built upon some of the technology you might already be familiar with, like NumPy, pandas, and Matplotlib! As you build robust Machine Learning programs, it’s helpful to have all the sklearn commands all in one place in case you forget.

Why Use Machine Learning? The year is 2049… New York is overrun by bots and web crawlers. The capabilities of Machine Learning have reached new heights and the world as we know it will never be the same. Facial recognition technology that helps users tag and share photos of friends can now tag future friends; night drones are on the prowl. Machine learning powered self-driving cars (and flying cars) are now massively available to consumers. The steering wheel has become a thing of the past. Recommendation engines that suggest what VR shows to watch and what products to buy will now display a different environment for each user group. At the dawn of a new age, you can’t help but wonder, what is Machine Learning and how did it pivot our world so drastically?

I think you got a little bit knowledge about Data Science and Machine Learning from the key notes I have posted so far. public poll Yes, I have got the difference and similarities of both. – 12 👍👍👍👍👍👍👍 80% Meti, / /\, @Annanjr, @DerejeK, Lenjiso, Shubham, Abhinav, @Until_9, @met_asploit, anonymous, @Nathan_elnati, @Jollya_Iru Yes, but I am confused – 3 👍👍 20% Lil, @Habibelias, @Programmercplusplus 👥 15 people voted so far.

Similarities Perhaps the most similar concept of data science and machine learning is that they both touch the model. The main skills that both fields share are: ✅ SQL ✅ Python ✅ GitHub ✅ Concept of training and evaluating data The comparisons are primarily in programming; the languages each person uses to perform their respective roles. Both positions perform some form of engineering, whether that be a data scientist querying a database using SQL or the machine learning engineer using SQL to insert the suggestions or predictions from the model back into a newly labeled column/field. ✍Both fields require knowledge of Python (or R) and usually version control, code sharing, and pull requests through GitHub. ✍A machine learning engineer can sometimes want to know learn how the algorithms work like XGBoost or Random Forest, for example, and will need to look at the model’s hyperparameters for tuning in order to conduct research on memory and size constraints. While data scientists can build highly accurate models in academia or on the job, there can be more restrictions in the workplace due to time, money, and memory restraints. Differences Some of the differences are already outlined in the above sections of data science and machine learning, but there are some key features of both careers and academic research that are important to point out: ✍Data Science - focuses on statistics and algorithms - unsupervised and supervised algorithms - regression and classification - interprets results - presents and communicates results ✍Machine Learning - focus on software engineering and programming - automation - scaling - scheduling - incorporating model results into a table/warehouse/UI Education Not only can the two roles differ in the workplace, but in academia/education as well. There are different routes to becoming a data scientist and machine learning engineer. A data scientist might focus on that degree itself, statistics, mathematics, or actuarial science, whereas a machine learning engineer will have their main focus on software engineering (and some institutions do offer specifically machine learning as a certificate or degree).