Data Science a Machine Learning
Zvládate prácu s dátami na výbornú. Čo tak sa posunúť trocha vyššie? Kurz je z polovice Data Science a z druhej polovice Machine Learning. Zameranie kurzu je na praktickosť a inžinierske porozumenie.
Pandas Introduction - Part 1
Pandas introduction - part 2
Cheatsheets for Data Science
Pandas Exercises
Data Science Quiz #1
[coding] Exercises solutions
Metodológia Data Science - Úvod
Metodológia Data Science - Data Collection, part 1
Metodológia Data Science - Data Collection, part 2
[Coding] Data Collection - Obtaining data
[Coding] Data Collection - Web Scraping
Additional resources
Data Collection - Exercises
Data Collection - Exercises, SOLUTION
Data Science Quiz #2
Exploratory Data Analysis (EDA) - Intro
Loading data & Summary
Missing values
Transformations
Visual Analyses
Correlation Map
[Coding] Exercise
Data Science Quiz #3
[coding] Exercises solutions
Data Visualisation story by Hans Rosling
DataViz - Introduction
DataViz - Plotting with Pandas
DataViz - Plotting with Matplotlib
[coding] Exercises - Pyplot
DataViz - Plotting with Seaborn
DataViz - Geoplot & Plotly
[coding] Exercises - Seaborn
Data Science Quiz #4
[coding] Exercises solutions
Fitting Data - Intro & Theory
Fitting Data - Interpolation
Fitting Data - Smoothing
Fitting Data - Curve Fitting
Fitting Data - Linear Regression
[coding] Exercises - Fitting
Data Science Quiz #5
[coding] Exercises solutions
Úvod do Machine Learningu (1)
Úvod do Machine Learningu (2)
Úvod do Machine Learningu (3)
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Fundamental elements of the ML
ML in practice - overview basic coding examples
Intro ML - Quiz
[Advanced] Coding: classify flowers (not Iris!)
Aplikovanie Machine Learning (ML) - Intro
Aplikovanie ML - Linear Model with Scikit-Learn
Aplikovanie ML - Missing values
Aplikovanie ML - Handling categorical values
Aplikovnie ML - Standardization and normalization
Aplikovanie ML - Training and evaluating model
Aplikovanie ML - Quiz
Aplikovanie ML - Coding exercise
Project Description
Template materials
Rozhodovacie stromy
Entropy and information gain
Random forests
Decision trees - coding
Random forests - coding
Coding excercise
Naive Bayes
k-Nearest neighbors
Logistic regression
Coding - EDA
Coding - Naive bayes
Coding - k-Nearest neighbors
Coding - Logistic regression
[coding] Exercises
[coding] Exercises Solutions
Quiz
Zhlukovanie - Intro
Coding - intro
Coding - Hierarchical clustering
Coding - KMeans and DBSCAN
[Exercise] - KMeans over Flowers
[Exercise] Solutions
Quiz
Hyperparameters tuning - Intro
Coding - Vanilla aka DIY hyperparameters tuning
Coding - Gridsearch and Randomsearch
Excercise - Bayesian optimization
[Theory] PCA algorithm
[Coding] Toy dataset
[Coding] MNIST dataset
[Exercise] Faces dataset
[Exercise] Solutions
Recommender Systems - Introduction
Recommender Systems - Collaborative-filtering in practice
Excercise 1: Matrix-factorization
Recommender Systems - Item-based filtering
Excercise 2: Hybrid recommender
Excercises 1&2: Solutions
[Theory] Neural Networks part 1
[Theory] Neural Networks part 2
[Coding] Pre-trained Neural Net - ResNet 50
[Exercises] Mobile Net & items on the table
[Exercises] Solutions
Keras - quick introduction
Keras - Data loading & preprocessing
Keras - Building the model using Functional API
Keras - Image classification from scratch