Important Tips on ML (DS310323 & DS150323

#Why Machine Learning introduced ? - Alan Turing - Computing Machinery and Intelligence
1.Diff Between ML and DL? -----------Fresher
2.What are different types of ML?-------Fresher
3.Overfitting and how can you avoid it? -----------experienced
3.Training set and test set? -------fresher
5.How do you handle missing or currupted data in dataset? -------Fresher
6.How can you choose a Classifier Based on A training set data size?--------Experienced
7.Confusion matrix? -----Freshers
Accuracy = (True positives + True Negatives)/ (True positives + True negatives + False positives + False negatives)

8.What is semi-supervised learning------- both fresher and ex
9.Unsupervised machine learning teachniques?

  1. comparison between K-means and KNN?
    11.Explain how a system can play a game of chess using reinforcement learning?
    12.Which machine learning algorithm you choose for your classification problem?
    13.How recommedation engine work?
    14.When you will use classification over regression?
    15.Random forest?
    16.Bais and variance?
    17.Precision score and recall score
    18.Decision tree classifier?
    19.pruning in decision tree?
    20.Logistic regression?
    21.kernel SVM ?
  2. PCA?
    23.F1 Score? ------------------F1=2*(p*r)/(p+r)
    24.Support vector in SVM?
    25.type I and type II error?
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