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.

Popis kurzu

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

About this course

€499,00
109 lekcie
20 hod videoobsahu

Data Science a Machine Learning

Popis kurzu

1. Základné nástroje pre Data Science (DS)

2. Metodológia pre DS

3. Exploratívna Analýza dát

4. Vizualizácia dát

5. Fitovanie a Regresia

6. Úvod do Machine Learningu

7. Aplikovanie Machine Learning

Project: Heart stroke prediction

8. Rozhodovacie stromy, random forests a ensemble metody

9. Klasifikátory - Naive Bayes, KNN, Logreg

10. Klustrovanie (zhlukovanie)

11. Vylaďovanie modelov a ich parametrov

12. Vysokorozmerné dáta a Redukcia dimenzionality

13. Odporúčacie systémy

14. Neurónové siete

15. Praktická aplikácia Neurónových sietí pomocou knižnice Keras

About this course