Artificial Intelligence

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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Interview QnAs For ML Engineer 1.What are the various steps involved in an data analytics project? The steps involved in a data analytics project are: Data collection Data cleansing Data pre-processing EDA Creation of train test and validation sets Model creation Hyperparameter tuning Model deployment 2. Explain Star Schema. Star schema is a data warehousing concept in which all schema is connected to a central schema. 3. What is root cause analysis? Root cause analysis is the process of tracing back of occurrence of an event and the factors which lead to it. It’s generally done when a software malfunctions. In data science, root cause analysis helps businesses understand the semantics behind certain outcomes. 4. Define Confounding Variables. A confounding variable is an external influence in an experiment. In simple words, these variables change the effect of a dependent and independent variable. A variable should satisfy below conditions to be a confounding variable : Variables should be correlated to the independent variable. Variables should be informally related to the dependent variable. For example, if you are studying whether a lack of exercise has an effect on weight gain, then the lack of exercise is an independent variable and weight gain is a dependent variable. A confounder variable can be any other factor that has an effect on weight gain. Amount of food consumed, weather conditions etc. can be a confounding variable.

Machine learning is a subset of artificial intelligence that involves developing algorithms and models that enable computers to learn from and make predictions or decisions based on data. In machine learning, computers are trained on large datasets to identify patterns, relationships, and trends without being explicitly programmed to do so. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on labeled data, where the correct output is provided along with the input data. Unsupervised learning involves training the algorithm on unlabeled data, allowing it to identify patterns and relationships on its own. Reinforcement learning involves training an algorithm to make decisions by rewarding or punishing it based on its actions. Machine learning algorithms can be used for a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, predictive analytics, and more. These algorithms can be trained using various techniques such as neural networks, decision trees, support vector machines, and clustering algorithms. Join for more: t.me/datasciencefun

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