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Which of the following python library is used for data visualization?
Anonymous Quiz
92%
Matplotlib
8%
Numpy
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365 data science courses for free till Nov 20
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Many people in telegram are unnecessarily charging huge money for this course
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Many people in telegram are unnecessarily charging huge money for this course
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Top 20 Pandas Interview Questions with Answers
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https://datasimplifier.com/pandas-interview-questions-with-answers/
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Which function is used to read CSV file in Pandas?
Anonymous Quiz
7%
read_file()
85%
read_csv()
5%
readcsvfile()
3%
readfile()
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Forwarded from Data Science Projects
Python Science Projects.pdf_20231120_013618_0000.pdf
2.1 MB
Python Data Science Projects For Boosting Your Portfolio
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10 Things you need to become an AI/ML engineer:
1. Framing machine learning problems
2. Weak supervision and active learning
3. Processing, training, deploying, inference pipelines
4. Offline evaluation and testing in production
5. Performing error analysis. Where to work next
6. Distributed training. Data and model parallelism
7. Pruning, quantization, and knowledge distillation
8. Serving predictions. Online and batch inference
9. Monitoring models and data distribution shifts
10. Automatic retraining and evaluation of models
1. Framing machine learning problems
2. Weak supervision and active learning
3. Processing, training, deploying, inference pipelines
4. Offline evaluation and testing in production
5. Performing error analysis. Where to work next
6. Distributed training. Data and model parallelism
7. Pruning, quantization, and knowledge distillation
8. Serving predictions. Online and batch inference
9. Monitoring models and data distribution shifts
10. Automatic retraining and evaluation of models
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Software Engineers vs AI Engineers: 👊
Software engineers are often shocked when they learn of AI engineers' salaries. There are two reasons for this surprise.
1. The total compensation for AI engineers is jaw-dropping. You can check it out at AIPaygrad.es, which has manually verified data for AI engineers. The median overall compensation for a “Novice” is $328,350/year.
2. AI engineers are no smarter than software engineers. You figure this out only after a friend or acquaintance upskills and finds a lucrative AI job.
The biggest difference between Software and AI engineers is the demand for such roles. One role is declining, and the other is reaching stratospheric heights.
Here is an example.
Just last week, we saw an implosion of OpenAI after Sam Altman was unceremoniously removed from his CEO position. About 95% of their AI Engineers threatened to quit in protest. Rumor had it that these 700 engineers had an open job offer from Microsoft. 🚀
Contrast this with the events a few months back. Microsoft laid off 10,000 Software Engineers while setting aside $10B to invest in OpenAI. They cut these jobs despite making stunning profits in 2023.
In conclusion, these events underline a significant shift in the tech industry. For software engineers, it's a call to adapt and possibly upskill in AI, while companies need to balance AI investments with nurturing their current talent. The future of tech hinges on flexibility and continuous learning for everyone involved."
Software engineers are often shocked when they learn of AI engineers' salaries. There are two reasons for this surprise.
1. The total compensation for AI engineers is jaw-dropping. You can check it out at AIPaygrad.es, which has manually verified data for AI engineers. The median overall compensation for a “Novice” is $328,350/year.
2. AI engineers are no smarter than software engineers. You figure this out only after a friend or acquaintance upskills and finds a lucrative AI job.
The biggest difference between Software and AI engineers is the demand for such roles. One role is declining, and the other is reaching stratospheric heights.
Here is an example.
Just last week, we saw an implosion of OpenAI after Sam Altman was unceremoniously removed from his CEO position. About 95% of their AI Engineers threatened to quit in protest. Rumor had it that these 700 engineers had an open job offer from Microsoft. 🚀
Contrast this with the events a few months back. Microsoft laid off 10,000 Software Engineers while setting aside $10B to invest in OpenAI. They cut these jobs despite making stunning profits in 2023.
In conclusion, these events underline a significant shift in the tech industry. For software engineers, it's a call to adapt and possibly upskill in AI, while companies need to balance AI investments with nurturing their current talent. The future of tech hinges on flexibility and continuous learning for everyone involved."
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Harvard CS50 – Free Computer Science Course (2023 Edition)
Here are the lectures included in this course:
Lecture 0 - Scratch
Lecture 1 - C
Lecture 2 - Arrays
Lecture 3 - Algorithms
Lecture 4 - Memory
Lecture 5 - Data Structures
Lecture 6 - Python
Lecture 7 - SQL
Lecture 8 - HTML, CSS, JavaScript
Lecture 9 - Flask
Lecture 10 - Emoji
Cybersecurity
https://www.freecodecamp.org/news/harvard-university-cs50-computer-science-course-2023/
Kaggle community for data science project discussion: @Kaggle_Group
Here are the lectures included in this course:
Lecture 0 - Scratch
Lecture 1 - C
Lecture 2 - Arrays
Lecture 3 - Algorithms
Lecture 4 - Memory
Lecture 5 - Data Structures
Lecture 6 - Python
Lecture 7 - SQL
Lecture 8 - HTML, CSS, JavaScript
Lecture 9 - Flask
Lecture 10 - Emoji
Cybersecurity
https://www.freecodecamp.org/news/harvard-university-cs50-computer-science-course-2023/
Kaggle community for data science project discussion: @Kaggle_Group
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Data Analytics is giving away Bard Mastery Course for free
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Basics of Machine Learning 👇👇
Free Resources to learn Machine Learning: https://news.1rj.ru/str/free4unow_backup/587
Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types:
1. Supervised Learning: The algorithm is trained on a labeled dataset, learning to map input to output. For example, it can predict housing prices based on features like size and location.
2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing.
3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications.
Key concepts include:
- Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training.
- Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance.
- Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns.
- Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks.
In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.
Join @datasciencefun for more
ENJOY LEARNING 👍👍
Free Resources to learn Machine Learning: https://news.1rj.ru/str/free4unow_backup/587
Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types:
1. Supervised Learning: The algorithm is trained on a labeled dataset, learning to map input to output. For example, it can predict housing prices based on features like size and location.
2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing.
3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications.
Key concepts include:
- Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training.
- Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance.
- Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns.
- Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks.
In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.
Join @datasciencefun for more
ENJOY LEARNING 👍👍
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Python Science Projects.pdf_20231120_013618_0000.pdf
2.1 MB
Python Data Science Projects For Boosting Your Portfolio
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1: How would you preprocess and tokenize text data from tweets for sentiment analysis? Discuss potential challenges and solutions.
- Answer: Preprocessing and tokenizing text data for sentiment analysis involves tasks like lowercasing, removing stop words, and stemming or lemmatization. Handling challenges like handling emojis, slang, and noisy text is crucial. Tools like NLTK or spaCy can assist in these tasks.
2: Explain the collaborative filtering approach in building recommendation systems. How might Twitter use this to enhance user experience?
- Answer: Collaborative filtering recommends items based on user preferences and similarities. Techniques include user-based or item-based collaborative filtering and matrix factorization. Twitter could leverage user interactions to recommend tweets, users, or topics.
3: Write a Python or Scala function to count the frequency of hashtags in a given collection of tweets.
- Answer (Python):
4: How does graph analysis contribute to understanding user interactions and content propagation on Twitter? Provide a specific use case.
- Answer: Graph analysis on Twitter involves examining user interactions. For instance, identifying influential users or detecting communities based on retweet or mention networks. Algorithms like PageRank or Louvain Modularity can aid in these analyses.
- Answer: Preprocessing and tokenizing text data for sentiment analysis involves tasks like lowercasing, removing stop words, and stemming or lemmatization. Handling challenges like handling emojis, slang, and noisy text is crucial. Tools like NLTK or spaCy can assist in these tasks.
2: Explain the collaborative filtering approach in building recommendation systems. How might Twitter use this to enhance user experience?
- Answer: Collaborative filtering recommends items based on user preferences and similarities. Techniques include user-based or item-based collaborative filtering and matrix factorization. Twitter could leverage user interactions to recommend tweets, users, or topics.
3: Write a Python or Scala function to count the frequency of hashtags in a given collection of tweets.
- Answer (Python):
def count_hashtags(tweet_collection):
hashtags_count = {}
for tweet in tweet_collection:
hashtags = [word for word in tweet.split() if word.startswith('#')]
for hashtag in hashtags:
hashtags_count[hashtag] = hashtags_count.get(hashtag, 0) + 1
return hashtags_count
4: How does graph analysis contribute to understanding user interactions and content propagation on Twitter? Provide a specific use case.
- Answer: Graph analysis on Twitter involves examining user interactions. For instance, identifying influential users or detecting communities based on retweet or mention networks. Algorithms like PageRank or Louvain Modularity can aid in these analyses.
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7 Baby steps to start with Machine Learning:
1. Start with Python
2. Learn to use Google Colab
3. Take a Pandas tutorial
4. Then a Seaborn tutorial
5. Decision Trees are a good first algorithm
6. Finish Kaggle's "Intro to Machine Learning"
7. Solve the Titanic challenge
1. Start with Python
2. Learn to use Google Colab
3. Take a Pandas tutorial
4. Then a Seaborn tutorial
5. Decision Trees are a good first algorithm
6. Finish Kaggle's "Intro to Machine Learning"
7. Solve the Titanic challenge
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Question 1 : How would you approach building a recommendation system for personalized content on Facebook? Consider factors like scalability and user privacy.
- Answer: Building a recommendation system for personalized content on Facebook would involve collaborative filtering or content-based methods. Scalability can be achieved using distributed computing, and user privacy can be preserved through techniques like federated learning.
Question 2 : Describe a situation where you had to navigate conflicting opinions within your team. How did you facilitate resolution and maintain team cohesion?
- Answer: In navigating conflicting opinions within a team, I facilitated resolution through open communication, active listening, and finding common ground. Prioritizing team cohesion was key to achieving consensus.
Question 3 : How would you enhance the security of user data on Facebook, considering the evolving landscape of cybersecurity threats?
- Answer: Enhancing the security of user data on Facebook involves implementing robust encryption mechanisms, access controls, and regular security audits. Ensuring compliance with privacy regulations and proactive threat monitoring are essential.
Question 4 : Design a real-time notification system for Facebook, ensuring timely delivery of notifications to users across various platforms.
- Answer: Designing a real-time notification system for Facebook requires technologies like WebSocket for real-time communication and push notifications. Ensuring scalability and reliability through distributed systems is crucial for timely delivery.
- Answer: Building a recommendation system for personalized content on Facebook would involve collaborative filtering or content-based methods. Scalability can be achieved using distributed computing, and user privacy can be preserved through techniques like federated learning.
Question 2 : Describe a situation where you had to navigate conflicting opinions within your team. How did you facilitate resolution and maintain team cohesion?
- Answer: In navigating conflicting opinions within a team, I facilitated resolution through open communication, active listening, and finding common ground. Prioritizing team cohesion was key to achieving consensus.
Question 3 : How would you enhance the security of user data on Facebook, considering the evolving landscape of cybersecurity threats?
- Answer: Enhancing the security of user data on Facebook involves implementing robust encryption mechanisms, access controls, and regular security audits. Ensuring compliance with privacy regulations and proactive threat monitoring are essential.
Question 4 : Design a real-time notification system for Facebook, ensuring timely delivery of notifications to users across various platforms.
- Answer: Designing a real-time notification system for Facebook requires technologies like WebSocket for real-time communication and push notifications. Ensuring scalability and reliability through distributed systems is crucial for timely delivery.
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Latest job opportunities for data analyst, business analyst and data scientists
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