Machine Learning with Python

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🐼 20 of the most used Pandas + PDF functions 👨🏻‍💻 The first time I used Pandas, I was supposed to quickly clean and organize a raw and complex dataset with the help of Pandas functions. Using the groupby function, I was able to categorize the data and get in-depth analysis of customer behavior. Best of all, it was when I used loc and iloc that I could easily filter the data. ✔️ Since then I decided to prepare a list of the most used Pandas functions that I use on a daily basis. Now this list is ready! In the following, I will introduce 20 of the best and most used Pandas functions: 🏳️‍🌈 read_csv(): Fast data upload from CSV files 🏳️‍🌈 head(): look at the first five rows of the database to start.. 🏳️‍🌈 info(): Checking data structure such as data type and empty values. 🏳️‍🌈 describe(): Generate descriptive statistics for numeric columns. 🏳️‍🌈 loc[ ]: accesses rows and columns by label or condition. 🏳️‍🌈 iloc[ ]: Access data by row number. 🏳️‍🌈 merge(): Merge dataframes with common columns. 🏳️‍🌈 groupby(): Grouping for easier analysis. 🏳️‍🌈 pivot_table(): Summarize data in pivot table format. 🏳️‍🌈 to_csv(): Save data as a CSV file. 🏳️‍🌈 pd.concat(): Concatenate multiple dataframes in rows or columns. 🏳️‍🌈 pd.melt(): Convert wide format data to long format. 🏳️‍🌈 pd.pivot_table(): Create a pivot table with multiple levels. 🏳️‍🌈 pd.cut(): Split the data into specific intervals. 🏳️‍🌈 pd.qcut(): Sort data by percentage. 🏳️‍🌈 pd.merge(): Merge data in database style for advanced linking. 🏳️‍🌈 DataFrame.apply(): Apply a custom function to the data. 🏳️‍🌈 DataFrame.groupby(): Analyze grouped data. 🏳️‍🌈 DataFrame.drop_duplicates(): Drop duplicate rows. 🏳️‍🌈 DataFrame.to_excel(): Save data directly to Excel file. ┌ 🐼 Pandas Functions └ 📄 PDF #MachineLearning #DeepLearning #BigData #Datascience #ML #Pandas #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents http://t.me/codeprogrammer ⭐️

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▶️ The best project oriented videos 💻 To learn data science 👨🏻‍💻 The best way to learn data science is to combine theoretical learning with practical projects. After you have mastered the main concepts and tools, stop wasting your time and start learning project-based and doing real projects! ✅ Here I put 17 real data science projects in video form:👇 1️⃣ Making machine learning transparent with LINK ⬅️ AI 2️⃣ LINK practical guide ⬅️ PySpark 3️⃣ Construction of LINK model ⬅️ SRKGPT 4️⃣ Building a recovery system ⬅️ LINK 5️⃣ Strengthening small models ⬅️ (2) , (1) 6️⃣ Set Falon and Lama-2 ⬅️ (2) , (1) 7️⃣ Embedding data using LINK ⬅️ BERT 8️⃣ Compression of models ⬅️ LINK 9️⃣ Dynamic pricing in business ⬅️ LINK 1️⃣ Product embedding in big data ⬅️ LINK 1️⃣ Twitter recommender systems ⬅️ LINK 1️⃣ How the Twitter algorithm works ⬅️ LINK 1️⃣ Pinterest advertising systems ⬅️ LINK 1️⃣ Search for the nearest neighbor ⬅️ LINK 1️⃣ LINK controller ⬅️ PID 1️⃣ Training neural networks ⬅️ LINK ⚫️ Data dimensionality reduction with LINK ⬅️ PCA

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