Some helpful Data science projects for beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Text mining : https://www.kaggle.com/kanncaa1/applying-text-mining
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Text mining : https://www.kaggle.com/kanncaa1/applying-text-mining
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What is Python?
- Python is a programming language 🐍
- It's known for being easy to learn and read 📖
- You can use it for web development, data analysis, artificial intelligence, and more 💻🌐📊
- Python is like writing instructions for a computer in a clear and simple way 📝💡
- Python supports working with a lot of data, making it great for projects that involve big data and statistics 📈🔍
- It has a huge community, which means lots of support and resources for learners 🌍🤝
- Python is versatile; it's used in scientific fields, finance, and even in making movies and video games 🧪💰🎬🎮
- It can run on different platforms like Windows, macOS, Linux, and even Raspberry Pi 🖥️🍏🐧🍓
- Python has many libraries and frameworks that help speed up the development process for web applications, machine learning, and more 🛠️🚀
- Python is a programming language 🐍
- It's known for being easy to learn and read 📖
- You can use it for web development, data analysis, artificial intelligence, and more 💻🌐📊
- Python is like writing instructions for a computer in a clear and simple way 📝💡
- Python supports working with a lot of data, making it great for projects that involve big data and statistics 📈🔍
- It has a huge community, which means lots of support and resources for learners 🌍🤝
- Python is versatile; it's used in scientific fields, finance, and even in making movies and video games 🧪💰🎬🎮
- It can run on different platforms like Windows, macOS, Linux, and even Raspberry Pi 🖥️🍏🐧🍓
- Python has many libraries and frameworks that help speed up the development process for web applications, machine learning, and more 🛠️🚀
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Python Roadmap
Stage 1 - Learn Python basics: syntax, loops, conditionals, and data types.
Stage 2 - Study OOP concepts and Python modules.
Stage 3 - Practice file handling, exceptions, and JSON handling.
Stage 4 - Use virtual environments and package management.
Stage 5 - Explore libraries like NumPy, Pandas, and Matplotlib.
Stage 6 - Learn threading, multiprocessing, and decorators.
Stage 7 - Improve testing and debugging.
Stage 8 - Contribute to open-source projects.
🏆 – Python Developer
Stage 1 - Learn Python basics: syntax, loops, conditionals, and data types.
Stage 2 - Study OOP concepts and Python modules.
Stage 3 - Practice file handling, exceptions, and JSON handling.
Stage 4 - Use virtual environments and package management.
Stage 5 - Explore libraries like NumPy, Pandas, and Matplotlib.
Stage 6 - Learn threading, multiprocessing, and decorators.
Stage 7 - Improve testing and debugging.
Stage 8 - Contribute to open-source projects.
🏆 – Python Developer
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Join our WhatsApp Channel for the free Python Resources & Projects
👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
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https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
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Understanding Generative AI: It's Not AGI
What is Generative AI?
Generative AI refers to algorithms designed to generate new content — from text to images — based on patterns learned from a dataset. Technologies like GPT-4 and DALL-E are popular examples, extensively used for tasks ranging from writing articles to designing graphics.
How Does Generative AI Work?
1 Training: Generative AI models are trained on large datasets, learning the structure, style, and intricacies of the data without human intervention.
2 Pattern Recognition: Through training, these models recognize patterns and correlations in the data, enabling them to predict and generate similar outputs.
3 Output Generation: When provided with a prompt, generative AI uses its training to produce content that aligns with what it has learned, attempting to mimic the input style or respond to the query coherently.
Generative AI vs. AGI:
• Specialization: Generative AI excels in specific tasks it's trained for but lacks the ability to perform beyond its training.
• No Consciousness or Understanding: Unlike AGI, generative AI does not possess consciousness, understanding, or reasoning. It doesn't "think" like humans; it merely processes data based on pre-defined mathematical and probabilistic models.
• Task-Specific: Generative AI operates within the confines of its programming and training, contrasting with AGI's potential to perform any intellectual task that a human can.
Why It Matters:
Understanding the capabilities and limitations of generative AI helps set realistic expectations for its applications. It's a powerful tool for specific tasks but is far from the sci-fi notion of an all-knowing, all-purpose AI.
Generative AI is nowhere near AGI, it even works on different principles. It basically is an average function for non-numerical data. It can create an average text or an average picture from all the texts and pictures it has seen.
What is Generative AI?
Generative AI refers to algorithms designed to generate new content — from text to images — based on patterns learned from a dataset. Technologies like GPT-4 and DALL-E are popular examples, extensively used for tasks ranging from writing articles to designing graphics.
How Does Generative AI Work?
1 Training: Generative AI models are trained on large datasets, learning the structure, style, and intricacies of the data without human intervention.
2 Pattern Recognition: Through training, these models recognize patterns and correlations in the data, enabling them to predict and generate similar outputs.
3 Output Generation: When provided with a prompt, generative AI uses its training to produce content that aligns with what it has learned, attempting to mimic the input style or respond to the query coherently.
Generative AI vs. AGI:
• Specialization: Generative AI excels in specific tasks it's trained for but lacks the ability to perform beyond its training.
• No Consciousness or Understanding: Unlike AGI, generative AI does not possess consciousness, understanding, or reasoning. It doesn't "think" like humans; it merely processes data based on pre-defined mathematical and probabilistic models.
• Task-Specific: Generative AI operates within the confines of its programming and training, contrasting with AGI's potential to perform any intellectual task that a human can.
Why It Matters:
Understanding the capabilities and limitations of generative AI helps set realistic expectations for its applications. It's a powerful tool for specific tasks but is far from the sci-fi notion of an all-knowing, all-purpose AI.
Generative AI is nowhere near AGI, it even works on different principles. It basically is an average function for non-numerical data. It can create an average text or an average picture from all the texts and pictures it has seen.
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Here are 5 key Python libraries/ concepts that are particularly important for data analysts:
1. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data. Pandas offers functions for reading and writing data, cleaning and transforming data, and performing data analysis tasks like filtering, grouping, and aggregating.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is often used in conjunction with Pandas for numerical computations and data manipulation.
3. Matplotlib and Seaborn: Matplotlib is a popular plotting library in Python that allows you to create a wide variety of static, interactive, and animated visualizations. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive and informative statistical graphics. These libraries are essential for data visualization in data analysis projects.
4. Scikit-learn: Scikit-learn is a machine learning library in Python that provides simple and efficient tools for data mining and data analysis tasks. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. Scikit-learn also offers tools for model evaluation, hyperparameter tuning, and model selection.
5. Data Cleaning and Preprocessing: Data cleaning and preprocessing are crucial steps in any data analysis project. Python offers libraries like Pandas and NumPy for handling missing values, removing duplicates, standardizing data types, scaling numerical features, encoding categorical variables, and more. Understanding how to clean and preprocess data effectively is essential for accurate analysis and modeling.
By mastering these Python concepts and libraries, data analysts can efficiently manipulate and analyze data, create insightful visualizations, apply machine learning techniques, and derive valuable insights from their datasets.
Credits: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
1. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data. Pandas offers functions for reading and writing data, cleaning and transforming data, and performing data analysis tasks like filtering, grouping, and aggregating.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is often used in conjunction with Pandas for numerical computations and data manipulation.
3. Matplotlib and Seaborn: Matplotlib is a popular plotting library in Python that allows you to create a wide variety of static, interactive, and animated visualizations. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive and informative statistical graphics. These libraries are essential for data visualization in data analysis projects.
4. Scikit-learn: Scikit-learn is a machine learning library in Python that provides simple and efficient tools for data mining and data analysis tasks. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. Scikit-learn also offers tools for model evaluation, hyperparameter tuning, and model selection.
5. Data Cleaning and Preprocessing: Data cleaning and preprocessing are crucial steps in any data analysis project. Python offers libraries like Pandas and NumPy for handling missing values, removing duplicates, standardizing data types, scaling numerical features, encoding categorical variables, and more. Understanding how to clean and preprocess data effectively is essential for accurate analysis and modeling.
By mastering these Python concepts and libraries, data analysts can efficiently manipulate and analyze data, create insightful visualizations, apply machine learning techniques, and derive valuable insights from their datasets.
Credits: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
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