Lesson 1
Installing python
Anaconda installation and review of packages
Working with jupyter and spyder notebooks
Variables, base types
Basic String Methods and arithmetic operations
Indexing, slicing, formatting
Homework and feedback
Lesson 2
Lists, tuples, dictionaries, and their methods
Comparison Operators
Homework and feedback
Lesson 3
Simple and nested functions
*args and *kwargs
Homework and feedback
Lesson 4
Control flow
Nested if conditions
if-elif-else
for while loops and their statements
Homework and feedback
Lesson 5
Looping and Unpacking with Dictionaries , List and Tuples
List, Zip, shuffle functions and methods
Input function, and random package.
Polymorphism and try except
Homework and feedback
Lesson 6
Introduction to Machine Learning libraries
Numpy
Pandas
Lesson 7
Visualization of data with Matplotlib
Visualization of data with Seaborn, Distribution and Categorical plots, Matrix Plots and Grids, Regression plots, styles and colors
Visualization of data with Plotly and Cufflinks
Part 2
Machine and Deep Learning topics
Lesson 8
Practice
Lesson 9
Introduction to Data Science, Machine Learning and Deep Learning
Lesson 10
Simple and Multiple Linear Regression
Conversions (categorical to numerical and numerical to categorical )
Complete data cleaning
Pre processing and modeling steps using real data
Lesson 11
Practice
Lesson 12
Logistic Regression and main application fields (IFRS9 provision calculation)
Lesson 13
Practice
Lesson 14
Decision tree and Random Forest regression vs classification
Hyperparameter optimization
Randomizer Search and Grid Search
Lesson 15
Practice
Lesson 16
LightGBM
Catboost
XGBoost Regression vs Classification and Stacking regression vs classification
Optuna parameter optimization
Lesson 17
Practice
Lesson 18
Unsupervised Learning application fields
Principal component analysis (PCA) and
Clustering models (DBSCAN, K-Means, Hierarchical Clustering)
Lesson 19
Practice
Lesson 20
Introduction to Supervised Deep Learning
Artificial Neural Networks
Lesson 21
Practice
Lesson 22
Convolutional Neural Networks
Lesson 23
Practice
Lesson 24
Recurrent Neural Networks
Lesson 25
Practice
Lesson 26
Introduction to Unsupervised Deep Learning,
Self Organizing Maps
Lesson 27
Practice
Lesson 28
Deep Boltzmann Machines
Energy-based models
Restricted Boltzmann machines for recommender systems
Lesson 29
Practice
Lesson 30
Auto Encoders (Sparse, Denoising, Contractive, Stacking and Deep autoencoders)
Lesson 31
Practice
Lesson 32
Final Exam (Quiz and Practical)