Topics Covered - Vector-Programming/VectorSessions GitHub Wiki

Session 1: Intro to Python

  1. Variables
  2. Hello World run through
  3. Datatypes and Operators in Python
  4. If Else statements
  5. For, While Loops
  6. Functions in Python
  7. Lists and Tuples in Python

Session 2: Basics And Arrays

  1. Basic Numpy Syntax and built-in functions.
  2. Multidimensional Arrays.
  3. Iteration through arrays with For loops.
  4. Multidimensional Array Construction.

Notes: https://goo.gl/VpzguW

Question 4 Explained: https://drive.google.com/open?id=1uPv0mH_ccUMpcBFY72LTW2WEg0T2Lc4W

Session 3: Classes and Objects

  1. What are Classes?
  2. What are objects?
  3. What is OOP?
    Make sure you read up on those topics separately.
  4. Example of creating a class based on a Video Game character.
  5. Method Creation
  6. PyGame example to visualize a ball class.

Notes: https://goo.gl/c7KdLy

Session Recording: https://drive.google.com/open?id=1sjZCuvIfzOqCgN_akkh3cIt66BpiARVn

Session 4: Intro to Machine Learning

  1. What is Machine Learning?
  2. What are the two main categories of ML?
  3. What are some examples of ML?
  4. How does ML "work"?
  5. What are the benefits and drawbacks of scikit-learn?
  6. How do I install scikit-learn?
  7. How do I use the IPython Notebook?
  8. What is the famous iris dataset, and how does it relate to machine learning?
  9. How do we load the iris dataset into scikit-learn?
  10. How do we describe a dataset using machine learning terminology?
  11. What are scikit-learn's four key requirements for working with data?
  12. What is the K-nearest neighbors classification model?
  13. What are the four steps for model training and prediction in scikit-learn?
  14. How can I apply this pattern to other machine learning models?

Notes: https://goo.gl/Qj5nMW

Session Recording: https://drive.google.com/drive/folders/1GC8htnvkabmtdvH9txMDV7-Q8fowmAxr

Session 5: Intro to Algorithms and CS Fundamentals

  1. What are algorithms?
  2. How are they defined in terms of CS?
  3. When is an algorithm said to be correct?
  4. Examples of Algorithms used in real life problems.
  5. How to solve the basic searching problem?
  6. Binary Search
  7. How to do Binary Search? How to sort?
  8. Insertion sort coded in Python.
    Read up more on time complexity

Session Recording: https://drive.google.com/open?id=1JjWlavbPB-opoz2NNBG2SynUSkJSIUw1