Data Science & Machine Learning

7 Steps of the Machine Learning Process Data Collection: The process of extracting raw datasets for the machine learning task. This data can come from a variety of places, ranging from open-source online resources to paid crowdsourcing. The first step of the machine learning process is arguably the most important. If the data you collect is poor quality or irrelevant, then the model you train will be poor quality as well. Data Processing and Preparation: Once you’ve gathered the relevant data, you need to process it and make sure that it is in a usable format for training a machine learning model. This includes handling missing data, dealing with outliers, etc. Feature Engineering: Once you’ve collected and processed your dataset, you will likely need to transform some of the features (and sometimes even drop some features) in order to optimize how well a model can be trained on the data. Model Selection: Based on the dataset, you will choose which model architecture to use. This is one of the main tasks of industry engineers. Rather than attempting to come up with a completely novel model architecture, most tasks can be thoroughly performed with an existing architecture (or combination of model architectures). Model Training and Data Pipeline: After selecting the model architecture, you will create a data pipeline for training the model. This means creating a continuous stream of batched data observations to efficiently train the model. Since training can take a long time, you want your data pipeline to be as efficient as possible. Model Validation: After training the model for a sufficient amount of time, you will need to validate the model’s performance on a held-out portion of the overall dataset. This data needs to come from the same underlying distribution as the training dataset, but needs to be different data that the model has not seen before. Model Persistence: Finally, after training and validating the model’s performance, you need to be able to properly save the model weights and possibly push the model to production. This means setting up a process with which new users can easily use your pre-trained model to make predictions.

©How fresher can get a job as a data scientist?© India as a job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from? The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice. Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way. All the major data science jobs for freshers will only be available through off-campus interviews. Some companies that hires data scientists are: Siemens Accenture IBM Cerner Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.

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Neural Networks and Learning Machines Third Edition 👇👇 https://cours.etsmtl.ca/sys843/REFS/Books/ebook_Haykin09.pdf

Mathematics for Machine Learning Published by Cambridge University Press (published April 2020) https://mml-book.com PDF: https://mml-book.github.io/book/mml-book.pdf

Well done guys!! Explanation for those who marked wrong answer: Read the question again The Answer to (9//2) is 4 and not 4.5

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Data Analysis with Python from Scratch 👇👇 https://t.me/sqlspecialist/26

❤️ Learning Path for ML

Data Science Interview Questions [PART-16] Q. How can outlier values be treated? A. An outlier is an observation in a dataset that differs significantly from the rest of the data. This signifies that an outlier is much larger or smaller than the rest of the data. Given are some of the methods of treating the outliers: Trimming or removing the outlier, Quantile based flooring and capping, Mean/Median imputation. Q. What is root cause analysis? A. A root cause is a component that contributed to a nonconformance and should be eradicated permanently through process improvement. The root cause is the most fundamental problem—the most fundamental reason—that puts in motion the entire cause-and-effect chain that leads to the problem (s). Root cause analysis (RCA) is a word that refers to a variety of approaches, tools, and procedures used to identify the root causes of problems. Some RCA approaches are more directed toward uncovering actual root causes than others, while others are more general problem-solving procedures, and yet others just provide support for the root cause analysis core activity. Q. What is bias and variance in Data Science? A. The model's simplifying assumptions simplify the target function, making it easier to estimate. Bias is the difference between the Predicted Value and the Expected Value in its most basic form. Variance refers to how much the target function's estimate will fluctuate as a result of varied training data. In contrast to bias, variance occurs when the model takes into account the data's fluctuations, or noise. Q. What is a confusion matrix? A. A confusion matrix is a method of summarising a classification algorithm's performance. Calculating a confusion matrix can help you understand what your classification model is getting right and where it is going wrong. This gives us the following: "True positive" for event values that were successfully predicted. "False positive" for event values that were mistakenly predicted. For successfully anticipated no-event values, "true negative" is used. "False negative" for no-event values that were mistakenly predicted. ENJOY LEARNING 👍👍