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Showing posts with the label machine learning algorithms

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Top Questions People Ask About Pandas, NumPy, Matplotlib & Scikit-learn — Answered!

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 Whether you're a beginner or brushing up on your skills, these are the real-world questions Python learners ask most about key libraries in data science. Let’s dive in! 🐍 🐼 Pandas: Data Manipulation Made Easy 1. How do I handle missing data in a DataFrame? df.fillna( 0 ) # Replace NaNs with 0 df.dropna() # Remove rows with NaNs df.isna(). sum () # Count missing values per column 2. How can I merge or join two DataFrames? pd.merge(df1, df2, on= 'id' , how= 'inner' ) # inner, left, right, outer 3. What is the difference between loc[] and iloc[] ? loc[] uses labels (e.g., column names) iloc[] uses integer positions df.loc[ 0 , 'name' ] # label-based df.iloc[ 0 , 1 ] # index-based 4. How do I group data and perform aggregation? df.groupby( 'category' )[ 'sales' ]. sum () 5. How can I convert a column to datetime format? df[ 'date' ] = pd.to_datetime(df[ 'date' ]) ...

20 Best Videos to Learn Machine Learning Quickly

According to Coursera -Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. 1.      introduction, The Motivation Applications of Machine Learning 2.      An Application of Supervised Learning - Autonomous Deriving 3.      The Concept of Underfitting and Overfitting 4.      Newtons Method 5.      Discriminative Algorithms 6.      Multinomial Event Model 7.      Optimal Margin Classifier 8.      Kernels 9.      Bias/variance Tradeoff 10. Uniform Convergence - The Case of Infinite H 11. Bayesian Statistics and Regularization 12. The Concept of Unsupervised Learning...