Artificial Intelligence

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Applications of Deep Learning

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Important LLM terms ✅

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©How fresher can get a job as a data scientist?© 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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Time Complexity of 10 Most Popular ML Algorithms . . When selecting a machine learning model, understanding its time complexity is crucial for efficient processing, especially with large datasets. For instance, 1️⃣ Linear Regression (OLS) is computationally expensive due to matrix multiplication, making it less suitable for big data applications. 2️⃣ Logistic Regression with Stochastic Gradient Descent (SGD) offers faster training times by updating parameters iteratively. 3️⃣ Decision Trees and Random Forests are efficient for training but can be slower for prediction due to traversing the tree structure. 4️⃣ K-Nearest Neighbours is simple but can become slow with large datasets due to distance calculations. 5️⃣ Naive Bayes is fast and scalable, making it suitable for large datasets with high-dimensional features.

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