Popis kurzu

  • 1

    1. Základné nástroje pre Data Science (DS)

    • Pandas Introduction - Part 1

    • Pandas introduction - part 2

    • Cheatsheets for Data Science

    • Pandas Exercises

    • Data Science Quiz #1

    • [coding] Exercises solutions
  • 2

    2. Metodológia pre DS

    • 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
  • 3

    3. Exploratívna Analýza dát

    • Exploratory Data Analysis (EDA) - Intro

    • Loading data & Summary

    • Missing values

    • Transformations

    • Visual Analyses

    • Correlation Map

    • [Coding] Exercise

    • Data Science Quiz #3

    • [coding] Exercises solutions
  • 4

    4. Vizualizácia dát

    • 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
  • 5

    5. Fitovanie a Regresia

    • 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
  • 6

    6. Úvod do Machine Learningu

    • 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!)
  • 7

    7. Aplikovanie Machine Learning

    • 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
  • 8

    Project: Heart stroke prediction

    • Project Description

    • Template materials
  • 9

    8. Rozhodovacie stromy, random forests a ensemble metody

    • Rozhodovacie stromy

    • Entropy and information gain

    • Random forests

    • Decision trees - coding

    • Random forests - coding

    • Coding excercise
  • 10

    9. Klasifikátory - Naive Bayes, KNN, Logreg

    • 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
  • 11

    10. Klustrovanie (zhlukovanie)

    • Zhlukovanie - Intro

    • Coding - intro

    • Coding - Hierarchical clustering

    • Coding - KMeans and DBSCAN

    • [Exercise] - KMeans over Flowers

    • [Exercise] Solutions

    • Quiz
  • 12

    11. Vylaďovanie modelov a ich parametrov

    • Hyperparameters tuning - Intro

    • Coding - Vanilla aka DIY hyperparameters tuning

    • Coding - Gridsearch and Randomsearch

    • Excercise - Bayesian optimization
  • 13

    12. Vysokorozmerné dáta a Redukcia dimenzionality

    • [Theory] PCA algorithm

    • [Coding] Toy dataset

    • [Coding] MNIST dataset

    • [Exercise] Faces dataset

    • [Exercise] Solutions
  • 14

    13. Odporúčacie systémy

    • 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
  • 15

    14. Neurónové siete

    • [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
  • 16

    15. Praktická aplikácia Neurónových sietí pomocou knižnice Keras

    • Keras - quick introduction

    • Keras - Data loading & preprocessing

    • Keras - Building the model using Functional API

    • Keras - Image classification from scratch