Course curriculum

    1. Multicollinearity

    2. Orthogonality

    3. How do VIF values help?

    4. EigenValues Multicollinearity

    5. Condition indices Multicollinearity

    6. Eigen values, PCA & Collinearity

    7. Explanatory variables are also called as...!

    8. Why care about regression analysis in the age of Neural Networks

    9. Types of regression

    10. Model fitting

    11. OLS intro

    12. Pearson Correlation

    13. Hypothesis Testing with Pearson Correlation

    14. Correlation does not imply Causation

    15. Takeaways of regression

    16. Variability & R2

    1. Into to Flask

    2. What's the theory behind Flask I must know?

    3. Where can I deploy Flask?

    4. Brief Overview of Flask

    5. Brief overview of HTML

    1. Computer_Vision most useful types of applications

    1. Entropy_overview

    2. Binomial & Poisson

    1. Bias & Variance

    2. Case of High Variance

    3. Understanding the Bias

    4. Difference Between Model & Algorithm

    1. Time Series Forecasting Methods

About this course

  • Free
  • 35 lessons
  • 3 hours of video content

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