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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' ]) ...
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20 Best Videos to Learn Machine Learning Quickly

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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
13. Mixture of Gaussian
14. The Factor Analysis Model
15. Latent Semantic Indexing (LSI)
16. Applications of Reinforcement Learning
17. Generalization to the Continuous States
18. State-action Rewards
19. Advice for Applying Machine Learning
20. Partially Observable MDPs (POMDPs)

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