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Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐ฒ ๐๐ฅ๐๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐๐๐๐๐ฟ๐ฒ-๐ฃ๐ฟ๐ผ๐ผ๐ณ ๐ฆ๐ธ๐ถ๐น๐น๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
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5 Handy Tips to Master Data Science โฌ๏ธ
1๏ธโฃ Begin with introductory projects that cover the fundamental concepts of data science, such as data exploration, cleaning, and visualization. These projects will help you get familiar with common data science tools and libraries like Python (Pandas, NumPy, Matplotlib), R, SQL, and Excel
2๏ธโฃ Look for publicly available datasets from sources like Kaggle, UCI Machine Learning Repository. Working with real-world data will expose you to the challenges of messy, incomplete, and heterogeneous data, which is common in practical scenarios.
3๏ธโฃ Explore various data science techniques like regression, classification, clustering, and time series analysis. Apply these techniques to different datasets and domains to gain a broader understanding of their strengths, weaknesses, and appropriate use cases.
4๏ธโฃ Work on projects that involve the entire data science lifecycle, from data collection and cleaning to model building, evaluation, and deployment. This will help you understand how different components of the data science process fit together.
5๏ธโฃ Consistent practice is key to mastering any skill. Set aside dedicated time to work on data science projects, and gradually increase the complexity and scope of your projects as you gain more experience.
1๏ธโฃ Begin with introductory projects that cover the fundamental concepts of data science, such as data exploration, cleaning, and visualization. These projects will help you get familiar with common data science tools and libraries like Python (Pandas, NumPy, Matplotlib), R, SQL, and Excel
2๏ธโฃ Look for publicly available datasets from sources like Kaggle, UCI Machine Learning Repository. Working with real-world data will expose you to the challenges of messy, incomplete, and heterogeneous data, which is common in practical scenarios.
3๏ธโฃ Explore various data science techniques like regression, classification, clustering, and time series analysis. Apply these techniques to different datasets and domains to gain a broader understanding of their strengths, weaknesses, and appropriate use cases.
4๏ธโฃ Work on projects that involve the entire data science lifecycle, from data collection and cleaning to model building, evaluation, and deployment. This will help you understand how different components of the data science process fit together.
5๏ธโฃ Consistent practice is key to mastering any skill. Set aside dedicated time to work on data science projects, and gradually increase the complexity and scope of your projects as you gain more experience.
๐5โค1
Natwest hiring Data Scientist
Apply link: https://jobs.natwestgroup.com/jobs/16064413-data-scientist?tm_job=R-00256693-OTHLOC-IND-5FCHE051&tm_event=view&tm_company=861&bid=56
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Forwarded from Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
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GE Aerospace is hiring!
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Salary: 8 - 16 LPA (Expected)
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Position: Data Scientist
Qualification: Bachelorโs/ Masterโs Degree
Salary: 8 - 16 LPA (Expected)
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Forwarded from Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books
๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ ๐ข๐ป ๐ฆ๐ผ๐ณ๐๐๐ฎ๐ฟ๐ฒ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐๐
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Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
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1. Explain the concept of transfer learning in the context of deep learning models. How can it be beneficial in practical applications?
Ans- Transfer learning involves leveraging pre-trained models on large datasets and adapting them to new, related tasks with smaller datasets. In deep learning, this is achieved by reusing the knowledge gained during the training of one model on a different, but related, task. This is particularly beneficial when the new task has limited labeled data.
Practical applications include image recognition, where a model pre-trained on a dataset like ImageNet can be fine-tuned for a specific domain. Transfer learning accelerates model convergence, requires less labeled data, and helps overcome the challenges of training deep neural networks from scratch.
2. Given a large dataset, how would you efficiently sample a representative subset for model training? Discuss the trade-offs involved.
Answer- To efficiently sample a representative subset, one can use techniques like random sampling or stratified sampling. For random sampling, simple random sampling or systematic sampling methods can be employed. For stratified sampling, data is divided into strata, and samples are randomly selected from each stratum.
Trade-offs involve the choice between biased and unbiased sampling. Random sampling may not capture rare events, while stratified sampling might introduce complexity but ensures representation. The size of the sample is also crucial; a too-small sample may not be representative, while a too-large sample may incur unnecessary computational costs.
3. How would you approach analyzing A/B test results to determine the effectiveness of a new feature on a platform like Google Search?
Answer: A/B testing involves comparing the performance of two versions (A and B) to determine the impact of a change. To analyze A/B test results:
- Define Metrics: Clearly define key metrics (e.g., click-through rate, user engagement) before the test.
- Random Assignment: Ensure random assignment of users to control (A) and experimental (B) groups.
- Statistical Significance: Use statistical tests (e.g., t-test) to determine if differences between groups are statistically significant.
- Practical Significance: Consider the practical significance of results to assess real-world impact.
- Segmentation: Analyze results across different user segments for nuanced insights.
4. You have access to search query logs. How would you identify and address potential biases in the search results?
Answer: To identify and address biases in search results:
- Analyze Demographics: Examine user demographics to identify biases related to age, gender, or location.
- Query Intent: Understand user query intent and ensure diverse queries are well-represented.
- Evaluate Results: Assess the diversity of results to avoid favoring specific perspectives.
- User Feedback: Gather feedback from users to identify biased or inappropriate results.
- Continuous Monitoring: Implement continuous monitoring and iterate on algorithms to minimize biases.
Ans- Transfer learning involves leveraging pre-trained models on large datasets and adapting them to new, related tasks with smaller datasets. In deep learning, this is achieved by reusing the knowledge gained during the training of one model on a different, but related, task. This is particularly beneficial when the new task has limited labeled data.
Practical applications include image recognition, where a model pre-trained on a dataset like ImageNet can be fine-tuned for a specific domain. Transfer learning accelerates model convergence, requires less labeled data, and helps overcome the challenges of training deep neural networks from scratch.
2. Given a large dataset, how would you efficiently sample a representative subset for model training? Discuss the trade-offs involved.
Answer- To efficiently sample a representative subset, one can use techniques like random sampling or stratified sampling. For random sampling, simple random sampling or systematic sampling methods can be employed. For stratified sampling, data is divided into strata, and samples are randomly selected from each stratum.
Trade-offs involve the choice between biased and unbiased sampling. Random sampling may not capture rare events, while stratified sampling might introduce complexity but ensures representation. The size of the sample is also crucial; a too-small sample may not be representative, while a too-large sample may incur unnecessary computational costs.
3. How would you approach analyzing A/B test results to determine the effectiveness of a new feature on a platform like Google Search?
Answer: A/B testing involves comparing the performance of two versions (A and B) to determine the impact of a change. To analyze A/B test results:
- Define Metrics: Clearly define key metrics (e.g., click-through rate, user engagement) before the test.
- Random Assignment: Ensure random assignment of users to control (A) and experimental (B) groups.
- Statistical Significance: Use statistical tests (e.g., t-test) to determine if differences between groups are statistically significant.
- Practical Significance: Consider the practical significance of results to assess real-world impact.
- Segmentation: Analyze results across different user segments for nuanced insights.
4. You have access to search query logs. How would you identify and address potential biases in the search results?
Answer: To identify and address biases in search results:
- Analyze Demographics: Examine user demographics to identify biases related to age, gender, or location.
- Query Intent: Understand user query intent and ensure diverse queries are well-represented.
- Evaluate Results: Assess the diversity of results to avoid favoring specific perspectives.
- User Feedback: Gather feedback from users to identify biased or inappropriate results.
- Continuous Monitoring: Implement continuous monitoring and iterate on algorithms to minimize biases.
๐2
Pricelabs hiring Data Scientist
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https://hello.pricelabs.co/careers?jobId=UPL41kc6rPZK&ft_source=3000178039&ft_medium=3000170988
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https://hello.pricelabs.co/careers?jobId=UPL41kc6rPZK&ft_source=3000178039&ft_medium=3000170988
PriceLabs
Careers
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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 Bayesโ 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 Bayesโ theorem, prior and posterior distributions, and Bayesian networks.
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Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
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Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
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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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Apply link: https://jobs.ebayinc.com/us/en/job/EBAEBAUSR0067204EXTERNALENUS/Machine-Learning-Engineer-T24
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๐ฑ ๐ฃ๐ผ๐๐ฒ๐ฟ๐ณ๐๐น ๐ฃ๐๐๐ต๐ผ๐ป ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ ๐๐ผ ๐๐ฑ๐ฑ ๐๐ผ ๐ฌ๐ผ๐๐ฟ ๐ฅ๐ฒ๐๐๐บ๐ฒ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
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Python projects are one of the best ways to showcase your skills and stand out in todayโs competitive job market๐ฃ๐
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Stand out in todayโs competitive job marketโ ๏ธ
Looking to land an internship, secure a tech job, or start freelancing in 2025?๐จโ๐ป
Python projects are one of the best ways to showcase your skills and stand out in todayโs competitive job market๐ฃ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4kvrfiL
Stand out in todayโs competitive job marketโ ๏ธ
Forwarded from Python for Data Analysts
๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐๐ถ๐ฐ๐ธ๐๐๐ฎ๐ฟ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ผ๐๐ฟ๐ป๐ฒ๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
Ready to upskill in data science for free?๐
Here are 3 amazing courses to build a strong foundation in Exploratory Data Analysis, SQL, and Python๐จโ๐ป๐
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Take the first step towards your dream career!โ ๏ธ
Ready to upskill in data science for free?๐
Here are 3 amazing courses to build a strong foundation in Exploratory Data Analysis, SQL, and Python๐จโ๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/43GspSO
Take the first step towards your dream career!โ ๏ธ