Key Concepts for Machine Learning Interviews
1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests.
2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE.
3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand.
4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees.
5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE).
6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization.
7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking.
8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis.
10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods.
11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients.
12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data.
13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment.
14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound.
15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayes’ theorem, prior and posterior distributions, and Bayesian networks.
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1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests.
2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE.
3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand.
4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees.
5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE).
6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization.
7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking.
8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis.
10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods.
11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients.
12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data.
13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment.
14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound.
15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayes’ theorem, prior and posterior distributions, and Bayesian networks.
I have curated the best interview resources to crack Data Science Interviews
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Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
𝟯 𝗙𝗿𝗲𝗲 𝗢𝗿𝗮𝗰𝗹𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗙𝘂𝘁𝘂𝗿𝗲-𝗣𝗿𝗼𝗼𝗳 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍
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Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
𝟱 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱 (𝗪𝗶𝘁𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀!)😍
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Ebay hiring Machine Learning Engineer
Apply link: https://jobs.ebayinc.com/us/en/job/EBAEBAUSR0067204EXTERNALENUS/Machine-Learning-Engineer-T24
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Forwarded from Python for Data Analysts
𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 𝗶𝗻 𝟮𝟬𝟮𝟱😍
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Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
𝗚𝗼𝗼𝗴𝗹𝗲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 - 𝗠𝗮𝘀𝘁𝗲𝗿 𝗛𝗶𝗴𝗵 𝗗𝗲𝗺𝗮𝗻𝗱 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 😍
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𝟓 𝐖𝐚𝐲𝐬 𝐭𝐨 𝐀𝐩𝐩𝐥𝐲 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐉𝐨𝐛𝐬
🔸𝐔𝐬𝐞 𝐉𝐨𝐛 𝐏𝐨𝐫𝐭𝐚𝐥𝐬
Job boards like LinkedIn & Naukari are great portals to find jobs.
Set up job alerts using keywords like “Data Analyst” so you’ll get notified as soon as something new comes up.
🔸𝐓𝐚𝐢𝐥𝐨𝐫 𝐘𝐨𝐮𝐫 𝐑𝐞𝐬𝐮𝐦𝐞
Don’t send the same resume to every job.
Take time to highlight the skills and tools that the job denoscription asks for, like SQL, Power BI, or Excel. It helps your resume get noticed by software that scans for keywords (ATS).
🔸𝐔𝐬𝐞 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧
Connect with recruiters and employees from your target companies. Ask for referrals when any jib opening is poster
Engage with data-related content and share your own work (like project insights or dashboards).
🔸𝐂𝐡𝐞𝐜𝐤 𝐂𝐨𝐦𝐩𝐚𝐧𝐲 𝐖𝐞𝐛𝐬𝐢𝐭𝐞𝐬 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲
Most big companies post jobs directly on their websites first.
Create a list of companies you’re interested in and keep checking their careers page. It’s a good way to find openings early before they post on job portals.
🔸𝐅𝐨𝐥𝐥𝐨𝐰 𝐔𝐩 𝐀𝐟𝐭𝐞𝐫 𝐀𝐩𝐩𝐥𝐲𝐢𝐧𝐠
After applying to a job, it helps to follow up with a quick message on LinkedIn. You can send a polite note to recruiter and aks for the update on your candidature.
🔸𝐔𝐬𝐞 𝐉𝐨𝐛 𝐏𝐨𝐫𝐭𝐚𝐥𝐬
Job boards like LinkedIn & Naukari are great portals to find jobs.
Set up job alerts using keywords like “Data Analyst” so you’ll get notified as soon as something new comes up.
🔸𝐓𝐚𝐢𝐥𝐨𝐫 𝐘𝐨𝐮𝐫 𝐑𝐞𝐬𝐮𝐦𝐞
Don’t send the same resume to every job.
Take time to highlight the skills and tools that the job denoscription asks for, like SQL, Power BI, or Excel. It helps your resume get noticed by software that scans for keywords (ATS).
🔸𝐔𝐬𝐞 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧
Connect with recruiters and employees from your target companies. Ask for referrals when any jib opening is poster
Engage with data-related content and share your own work (like project insights or dashboards).
🔸𝐂𝐡𝐞𝐜𝐤 𝐂𝐨𝐦𝐩𝐚𝐧𝐲 𝐖𝐞𝐛𝐬𝐢𝐭𝐞𝐬 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲
Most big companies post jobs directly on their websites first.
Create a list of companies you’re interested in and keep checking their careers page. It’s a good way to find openings early before they post on job portals.
🔸𝐅𝐨𝐥𝐥𝐨𝐰 𝐔𝐩 𝐀𝐟𝐭𝐞𝐫 𝐀𝐩𝐩𝐥𝐲𝐢𝐧𝐠
After applying to a job, it helps to follow up with a quick message on LinkedIn. You can send a polite note to recruiter and aks for the update on your candidature.
👍3
Forwarded from Python for Data Analysts
𝟯 𝗙𝗿𝗲𝗲 𝗧𝗖𝗦 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗘𝘃𝗲𝗿𝘆 𝗙𝗿𝗲𝘀𝗵𝗲𝗿 𝗦𝗵𝗼𝘂𝗹𝗱 𝗧𝗮𝗸𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍
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Forwarded from Python for Data Analysts
𝗚𝗼𝗼𝗴𝗹𝗲 𝗧𝗼𝗽 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
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Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
𝟱 𝗙𝗿𝗲𝗲 𝗢𝗿𝗮𝗰𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗹𝗼𝘂𝗱 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍
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Aeries is hiring Trainee - Data Science
For 2023, 2024, 2025 grads
Location: Hyderabad
https://aeriestechnology.talentrecruit.com/Search/Jobs/?2300-Trainee--%20Data%20Science
For 2023, 2024, 2025 grads
Location: Hyderabad
https://aeriestechnology.talentrecruit.com/Search/Jobs/?2300-Trainee--%20Data%20Science
Talentrecruit
Trainee - Data Science
Position/Title: Trainee - Data
Science
Location: Hyderabad
Department: Dev/Engineering
Employment type: Trainee
Experience: Fresher-2 years
Qualification: Graduate in Data Science, Compute...
Science
Location: Hyderabad
Department: Dev/Engineering
Employment type: Trainee
Experience: Fresher-2 years
Qualification: Graduate in Data Science, Compute...
Forwarded from Python for Data Analysts
𝟰 𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗿𝗼𝗺 𝗠𝗜𝗧, 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 & 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 𝘁𝗼 𝗟𝗮𝘂𝗻𝗰𝗵 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍
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OrangeShark hiring Data Scientist
Apply link: https://www.linkedin.com/jobs/view/4235056975
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All the best 👍👍
Apply link: https://www.linkedin.com/jobs/view/4235056975
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DeltaX is hiring Business Analyst 🚀 ( Intern )
Qualification : Any Graduate
Experience : Freshers
Location : Pune
Apply link : Check out this job at DeltaX: https://www.linkedin.com/jobs/view/4235059592
Qualification : Any Graduate
Experience : Freshers
Location : Pune
Apply link : Check out this job at DeltaX: https://www.linkedin.com/jobs/view/4235059592
Linkedin
DeltaX hiring Business Operations Analyst in Pune, Maharashtra, India | LinkedIn
Posted 6:45:33 AM. DeltaX is looking for an enthusiastic person to join its Business Operations / Account Management…See this and similar jobs on LinkedIn.
Google hiring Software Engineer
Apply link: https://careers.google.com/jobs/results/135150532506329798-software-engineer-iii
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👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
Apply link: https://careers.google.com/jobs/results/135150532506329798-software-engineer-iii
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍