Python Programming
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Introduction to the basics of computer programming
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Setup and buildup of familiarity of the platform to be used
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Python objects, variables and naming - basics
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Input and output handling
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Types of objects - strings, ints, floats, etc.
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Operations on each type of objects
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Meta Characters And Operators
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Python scripting
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Iterations and iterating tools
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Lists, Tuples, Dictionaries
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Simple algorithms and looping mechanisms/Control Statements
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Numerical computations and basics of accuracy
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Limitations of numerical computing
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Creation of functions
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Environments
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Global and local characteristics of variables
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Objects and classes - revisited in the context of functions
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Working with Series And DataFrame
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Testing and Debugging
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Error handling
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Assertions and exceptions
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Efficiency in programming and OOP
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Pitfalls to avoid
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Best practices
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Additional Concepts
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Case Study(Creation of a Strategy)
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Supporting Files and Details For Hands-On
Python for Financial Analysis & Algorithmic Trading
coming soon
Machine Learning
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Introduction to machine learning
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Intuition behind machine learning
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Supervised and unsupervised learning
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Matrix operations and their significance
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Linear regression with one variable
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Cost and reward functions
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Gradient descent and its Intuition
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Matrix operations for linear regression
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Linear regression with multiple variables
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Features and their significance
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Learning rate and its significance
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Adjusting learning rate on the fly
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Matrix operations for polynomial regression
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Classification / Logistic regression
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Cost functions and their derivations
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Optimization and gradient descent
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Binary and multi-class classification
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Regularization
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Regularization types
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Regularization in linear regression
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Regularization in logistic regression
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Preprocessing and data manipulation
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Feature engineering
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Feature extraction
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Dimensionality reduction
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Machine learning algorithms
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Support vector machines
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Stochastic gradient descent
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Nearest Neighbors
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Decision tress
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Ensemble methods
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Random forests
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Other methods
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Neural Networks
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Intuition behind neural networks
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Types of neural networks
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Perceptrons
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Other network types (eg: RNN, RL)
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Project Work
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.

