Machine Learning with Python

Data Visualization Cheat sheets and Resources Corpus of 32 DV cheat sheets, 32 DV charts and 7 recommended DV books

✅ A comprehensive playlist to step into and master the world of machine learning and data science! 1️⃣ Data Science Principles: 😉 Essential Mathematics for Machine Learning: Link 😉 Overview and commonly used terms: Link 😉 Current interview trends: Link 😉 Linear Regression Guide: Link 😉 Logistic Regression Playlist: Link 😉 Classification criteria: Link 😉 Simple Bayes Classifier: Link 😉 Types of variables: Link 😉 Dimension reduction: Link 😉 Entropy, mutual entropy, KL divergence: link 😉 Dynamic Pricing Overview: Link 2️⃣ Building recommender systems: 😉 Netflix Calibrated Recommendations: Link 😉 Netflix Integrated Recommendation Model: Link 😉 The Evolution of Recommender Systems: Link 😉 Embedding tutorial: Link 😉 Annoy library for approximate nearest neighbor: link 😉 Reducer product for ANN: Link 😉 Model-based account recommendations: Link 😉 PID controller for diversity: link 😉 Instagram Recommender System: Link 😉 LinkedIn CTR Modeling: Link 😉 Meituan's two-tower recommendation model: Link 😉 Scalable Two Tower Model Question-Item: Link 😉 Twitter Recommender Algorithm: Link 😉 eBay language model for recommender system: link 😉 Overcoming biases for recommender systems: Link 3️⃣ Advanced Model Techniques and Applications: 😉 Importance of Model Calibration: Link 😉 Detect and monitor data changes: Link 😉 Neural Networks Training: Link 😉 Analytics-based advertising with Pinterest: Link 😉 Using Pre-trained Bert: Link 😉 Model Compression with Knowledge Distillation: Link 😉 Multi-Armed Bandit Strategies: Link 4️⃣ The world of large language models (LLMs): 😉 Conversational AI: Link 😉 The dual nature of conversational language models: link 😉 Frontier Developments in LLM: Link 😉 Improving the performance of open source LLMs: Link 😉 Building artificial intelligence in Shah Rukh Khan style: Link 📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas http://t.me/codeprogrammer ⭐️

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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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