Python Programming

Module 1

  1. Introduction to the basics of computer programming

    1. Setup and buildup of familiarity of the platform to be used

  2. Python objects, variables and naming - basics

    1. Input and output handling

    2. Types of objects - strings, ints, floats, etc.

    3. Operations on each type of objects

  3. Python scripting

  4. Branching programs

  5. Iterations and iterating tools

    1. Lists, Tuples, Dictionaries

  6. Simple algorithms and looping mechanisms

  7. Numerical computations and basics of accuracy

    1. Limitations of numerical computing

  8. Creation of functions

  9. Environments

  10. Intermediate algorithms

    1. Bisection search

    2. Recursion

    3. Inductive reasoning

    4. Factorials

  11. Global and local characteristics of variables

  12. Objects and classes - revisited in the context of functions

  13. Testing and Debugging

  14. Error handling

    1. Assertions and exceptions

  15. Efficiency in programming and OOP

    1. Pitfalls to avoid

    2. Best practices

Machine Learning

Module 2

  1. Introduction to machine learning

    1. Intuition behind machine learning

    2. Supervised and unsupervised learning

    3. Matrix operations and their significance

  2. Linear regression with one variable

    1. Cost and reward functions

    2. Gradient descent and its Intuition

    3. Matrix operations for linear regression

  3. Linear regression with multiple variables

    1. Features and their significance

    2. Learning rate and its significance

      1. Adjusting learning rate on the fly

    3. Matrix operations for polynomial regression

  4. Classification / Logistic regression

    1. Cost functions and their derivations

    2. Optimization and gradient descent

    3. Binary and multi-class classification

  5. Regularization

    1. Regularization types

    2. Regularization in linear regression

    3. Regularization in logistic regression

  6. Preprocessing and data manipulation

    1. Feature engineering

    2. Feature extraction

    3. Dimensionality reduction

  7. Machine learning algorithms

    1. Support vector machines

    2. Stochastic gradient descent

    3. Nearest Neighbors

    4. Decision tress

    5. Ensemble methods

    6. Random forests

    7. Other methods

  8. Neural Networks

    1. Intuition behind neural networks

    2. Types of neural networks

    3. Perceptrons

    4. Other network types (eg: RNN, RL)

Project Work

Module 3

1. Automating Trading Strategies

2. Integrating Financial Models

3. Developing Backtesting UI

4. Scraping data for Stock Options or Index Options.

5. Predicting Stock Trends

6. Forecasting Indices.

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