My Road Map To Learning Python
This repository is a reflection of my journey in learning Python, starting from the beginning of 2018 when I first started learning the programming language. It showcases all the projects that I have created since then, as well as the skills and knowledge that I have acquired along the way.
The repository contains a variety of projects that demonstrate my proficiency in Python, covering a range of topics including web development, data analysis and machine learning. This repository is a reflection of my journey in learning Python, starting from the beginning of 2018 when I first started learning the programming language. It showcases all the projects that I have created since then, as well as the skills and knowledge that I have acquired along the way
Roadmap
Basics of Python
- Basic Syntax
- The Python shell, basic arithmetic.
- Control structures.
- Accepting user input, Strings & Typecasting.
- Looping in Python: For & While loops.
- Exception handling.
- Functions, modules & Imports.
- OOPs Concepts, Built-in Data Structures, and Other Stuff
- OOP Concepts
- Classes in Python
- Dunder
- Methods
- Generators
- Inheritance
- Functional Programming
- Lambda Functions
- Built-in Functions
- Decorators in Python
- Closures
- Regular Expressions in Python
- Lists & List function
- Regular Expressions
- List comprehension
- List slicing
- String formatting
- Lambdas
- List, Dictionaries, Sets, Stacks, Tuples
For Data Analysis and Machine Learning
- Data Visualization and Understanding Data
- What is Machine Learning
- Unsupervised vs Supervised Machine learning
- What are the types of Machine Learning Problems
- Basic Machine Learning terms and concepts
- Bias vs Variance Trade Off
- What is Underfitting and Overfitting
- Normalisation
- What is Reinforcement Learning
- Winsorising
- Gaussian Mixtures
- Log Error
- Time Series
- Ridge and Lasso Regularisation
- Hypothesis Function
- Linear Regression
- Gradient Descent
- Cat Booster
- Logistic Regression
- Support Vector Machines
- Dimensionality Reduction
- Decision Trees
- Ensemble Learning
- Random Forest
- Naive Bayes
- K-Means
- DBSCAN
- OPTICS
- CNN
- RNN
- Neural Networks and TensorFlow
- Deep Learning
- K-Cross Validation Techniques
- PROJECTS (Multiple Variations in each)
- Including - Data Visualisation, 2 Types of Machine Learning and Predictions
- Titanic
- Space Titanic
- Zillow Housing Prices
- Covid 19
- Fifa Worldcup
- Libraries Learnt
- Pandas
- Numpy
- MatplotLib
- Seaborne
- Keras
- Sci-kit Learn
- Preprocessing
- Model Selection
- cluster
- Tensorflow