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Delt with Chapters in the following order.

  1. Naive Bayes
  2. SVM
  3. Decision Trees
  4. Choose your wown algorithm
  5. DataSets and Questions.

Follow Folders under projects as well. As it is where most of the things are implemented.

Below here is supposed to be under naive bayes (folders prediction first / accuracy_second (GaussianNB)). But it is okay. (Russell Peters)Take it and go. Think of it as intro . But it actually describes the problem , Cancer test, test who have the cancer and who don't. well whatev.

Cancer Test

---------------------------

| | <-- All people | -- | | | |<--1% | 1% has Cancer | | | | 99 % don't | -- | 90% of 1% have positive test cancer | | 10 % of 99% of Non cancer people have Positive test. | |
| |
| | | | ---------------------------

Bayes Rule

prior probability * test evidence = posterior probability

Prior: prior probability of cancer P(c) = 0.01 1% P(NC) = 0.99 P(Pos | c) = 0.9 90% P(Neg | NC) 0.9 P(Pos | NC) = 0.1

Posterior: --> Often called as Joint posterior probability given test is positive P(c | pos) = P(c) * P(Pos | c) c--> cancer P(NC | pos) = P(NC) * P (Pos | NC) --> Positive test result for not cancer

P(c|pos) = 0.009
    P(NC|Pos) = 0.099
            + --------
                0.108

Normalize P(Pos) = P(c | pos) + P(NC | pos) =0.108

Posterior: P (c | Pos) = P (c | pos) / P (Pos) = 0.009 / 0.108 =0.0833 P (NC | pos) = P (NC|pos) / P(Pos) = 0.099 / 0.108 = 0.9166 +------- 1

P(C) Prior P(Pos | c) sensitivity P(neg | nc) specitivity

pos P(c) -- mul P (Pos|c)--> P(Pos,c) Positive cancer hypothesis P(c) -- mul { (pos|nc)--> P(Pos, nc) Positive non cancer hypothesis --add--> + P (Pos) total prob. that test is what it was divide P(Pos,c) / P (Pos) --> This gives posterior prob. P(C | Pos) and divide P(Pos, nc) /P(Pos)--> This is posterior prob . P(NC | Pos) if we add these up, posterior probability , it will sum to one.

This is BAYES RULE::

  1. Naive Bayes mini Project --install pip (If you dont have one) --go to your terminal line (don’t open python, just the command prompt) --install sklearn: pip install scikit-learn -- for your reference, here’s the sklearn installation instructions --install natural language toolkit: pip install nltk --Get the Intro to Machine Learning source code. You will need git to clone the repository: git clone https://github.com/udacity/ud120-projects.git

"You only have to do this once, the code base contains the starter code for all the mini-projects. Go into the tools/ directory, and run startup.py. It will first check for the python modules, then download and unzip a large dataset that we will use heavily later. The download/unzipping can take a while, but you don’t have to wait for it to finish in order to get started on Part 1."

Adarsh