This is a personal repository to teach Python, Machine Learning and Deep Learning(Basics)
To use this repository you have to install Anaconda and use Jupyter Notebook
Clarification: the content of this repository is not entirely created by me, some codes are a compilation of multiple courses that I have done.
- Datasets
- Directory of multiple collected Datasets
- Week 1 - Python Basics
- Variables types
- Operators
- Conditionals
- Week 2 - Python Basics 2
- Functions
- Dictionaries
- List
- Week 3 - Python Intermediate
- Inheritance
- Generators
- Iterators
- Modules
- Try Closures
- Serializer
- Week 4 - Intro Numpy
- Sets
- Numpy
- Week 5 - data visualization and manipulation
- Pandas
- Matplotlib
- Seaborn
- Bokeh
- Week 6 - Mathematics (Basics)
- Linear Algebra
- Probability and Statistics (see repository)
- Week 7 - Intro Machine Learning
- Linear Regression
- Polynomial Regression
- Logistic Regression
- Week 8 - Classification
- Overfit Complexity
- K Nearest Neighbor (KNN)
- Decision Tree
- Support Vector Machines (SVM)
- Week 9 - Classification 2
- K Means
- DBSCAN
- Recommender Systems
- Reduction of dimensionality (PCA, IPCA, KPCA, Regularization)
- Week 10 - Advanced Methods
- Robust Regressions
- Assembly Methods
- Cross Validation and K Folds
- Parametric Optimization
- Save and load ML Models
- Intro Deep Learning Part 1
- Week 11 - Intro Deep Learning
- Intro Deep Learning Part 2
- Deep Learning (Basic Theory)
- Pytorch
- Week 12 - NLP
- Natural Language Process
- NLTK
- Bonus
- Chatbot
- Sentiment Analysis