Հղումներ / Links
🔗 Հղումներ / Links
- Վիդեոները՝ Metric YouTube Channel
- Դասընթացին միանալու տելեգրամը՝ Հղում (կարաք երբ ուզում եք, ինչքան ժամանակով ուզում եք միանալ, ու դե անվճար ա իհարկե)
- Տնայինը՝ Profound Academy Python Introduction
- Լուծումները՝ կոդ/նշումներ, վիդեոներ
- Python-ի խառը լրացուցիչ նյութեր
- Ամեն տեսության վերջում դրած հղումները
- Տնայինի խնդիր առաջարկել որից կուզեք վիդեո սարքենք՝ հղում
- Դասընթացի վերաբերյալ կարծիք ու առաջարկներ՝ հղում
- հղում
🐍 Python
1 - Print, comments, փոփոխականներ, թվեր և թվաբանական գործողություններ, input
Done, add links later
2 - Conditions / Պայմաններ
Done, add links later
3 - String, list, range, functions on floats/lists
Done, add links later
4 - Loops / Ցիկլեր
Done, add links later
5 - List/String Methods + Ternary Operators, List Comprehensions
Done, add links later
6 - Tuple, Set, Dictionary
Done, add links later
7, 8 - Functions (գուցե երկու շաբաթ հատկացնենք)
Done, add links later
9 - Terminal, Working with multiple files, file I/O, Packages (os, random, time, tqdm)
Done, add links later ## 10 - Git / GitHub, Venvs, Anaconda + PEP8, clean code/architecture Done, add links later
11 - Exception Handling
Done, add links later
12 - Streamlit, Recustions, leftover material
Done, add links later
13 - Decorators
Done, add links later
14 OOP 1: Classes
Done, add links later
15 OOP 2: Inheritance, Polymorphism
Done, add links later
16 OOP 3: Encapsulation, Abstraction
Done, add links later
17 Data Classes, Generators, Iterators, Context Managers
Done, add links later
[] OpenAI API Video
June 25, Wednesday [] Practical, Classes
📦Packages
Data Science Packages
June 27, Friday [] NumPy + Smthing?
June 29, Sunday (perhaps skip)
July 2, Wednesday [] Pandas 1
July 4, Friday [] Pandas 2 + Profiling
July 6, Sunday (perhaps skip) [] Data Visualization
July 9, Wednesday [] Project 1
July 11, Friday [] Project 2
July 14 - 21 - Break
General Packages
[] Logging, Unittest (Pytest), Argparse (other CLI)
[] Scraping
[] Flask / FastAPI
[] Collections / functools [] pydantic [] Make [] docker [] pytest [] smthing argparse like [] dvc [] packaging [] zip [] smtp [] numba [] Sweetviz / pandas profiling
15 - Logging, Unittest (Pytest), Argparser
16 - Scraping
17 - Flask / FastAPI
18 - NumPy
19-20 - Pandas
21-22 - Data Visualization
23 - Some other packages (Streamlit, Dask, Sweetviz, Numba, …)
📈 Math
🧮 20-22.5 Linear Algebra
- Vectors, vector operations, dot product, norm
- Vector spaces and subspaces
- Matrices, matrix operations
- Geometric interpretation of matrices
- Row echelon form
- Determinant in 2x2 and 3x3 cases, trace
- Determinant in general case
- Systems of linear equations
- Gauss-Jordan elimination
- Inverse matrix
- Linear independence
- Basis, rank, dimension
- Eigenvalues and eigenvectors
- Positive/negative definite matrices
- Decompositions
📈 22.5 - 24 Calculus
- Limit of sequence and function
- Derivative
- Extrema of a function
- Taylor polynomials
- Indefinite integral, definite integral
- Partial derivative
- Gradient, directional gradient
- More topics
⛰️ 25 - 27 Optimization
- Quadratic forms and Sylvester’s criterion
- Gradient Descent
- Momentum
- AdaGrad / RMSProp / ADAM
- Second order methods
- Constrained optimiziation
- Evolutionary algorithms
- Bayesian optimization
- Multicriteria optimization
🎲 28 - 29 Probability Theory
- Sample space, events, probability
- Independence
- Conditional probability, total probability
- Bayes rule
- Geometric probability
- Random variable
- PMF, CDF, PDF
- Expected value, variance
- Covariance and correlation
- Distributions
- Laws of large numbers
- Central limit theorem
📊30 - 31 Statistics
- Point estimation: Mean, median, mode
- Estimator properties
- MAP / MLE
- Confidence intervals and hypothesis testing
- P-values, type I and type II errors
🤖 Machine Learning
32 Linear Regression
- Assumptions
- Loss
- Gradient based optimization
- Normal Equation
- Interpretation of Coefficients
33 - 34 Main Concepts
- Encoding categoricals
- Feature scaling
- Train Val Test split (data leakage issue)
- (Stratified) Cross validation
- Regression evaluation metrics
35 - 36 More Regression + Main Concepts 2
- Polynomial Regression
- Under / Overfitting
- Regularization
- Ridge
- Lasso
- Hyperparameter Search
- Feature Engineering
- Outliers
- Threshold tuning
37 Logistic Regression
- Logistic regression
- Log odds
- Classification evaluation metrics
38 Trees
- Decision tree
- Bagging
- Boosting
- Notable models (i.e. LightGBM)
39 Model interpretation and Feature selection
40 Unsupervised Learning
- KMeans
- DBSCAN
- Hierarchical
- Clustering evaluation metrics