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Game Development with Unity

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Deep Learning Odyssey

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Introduction to Neural Networks

Neural networks are powerful algorithms inspired by the structure of the human brain that are used to solve complex machine learning problems. You will build your own Neural Network from scratch to understand how it works. After this course, you will be able to create neural networks for solving classification and regression problems using the scikit-learn library.

ML Introduction with scikit-learn

Machine learning is now used everywhere. Want to learn it yourself? This course is an introduction to the world of Machine learning for you to learn basic concepts, work with Scikit-learn – the most popular library for ML and build your first Machine Learning project. This course is intended for students with a basic knowledge of Python, Pandas, and Numpy.

Introduction to NLP

Let's explore the fundamentals of Natural Language Processing (NLP) as you delve into text preprocessing techniques and various text models used to represent text data. You will gain practical insights and hands-on experience with the tools and methods essential for analyzing and interpreting textual data effectively. This course equips you with the skills to transform raw text into meaningful information, paving the way for advanced applications in AI and machine learning.

Classification with Python

In machine learning, classification is used in predictive modeling to assign input data with a class label. Sounds difficult? Don't worry! Let's cope with this! Welcome to the ML!

Cluster Analysis

Gain a solid understanding of cluster analysis, a key unsupervised learning technique for uncovering patterns in unlabeled data. Explore the essentials of K-Means, Hierarchical Clustering, DBSCAN, and GMMs, and get hands-on experience with real datasets to build confidence in applying clustering to real-world problems.

Introduction to RNNs

Master Recurrent neural networks and their advanced variants like LSTMs and GRUs using PyTorch. Gain hands-on experience processing sequential data for practical applications. Apply these powerful models to tackle real-world challenges in time series forecasting and various Natural language processing tasks.

Building a Book Recommendation System with Collaborative Filtering

Learn to build and evaluate a collaborative filtering recommender system using real-world user ratings data for personalized book recommendations.

Computer Vision Essentials

Comprehensive introduction to Computer Vision, focusing on machine perception and interpretation of visual data. Covers image preprocessing, feature extraction, object detection, and deep learning techniques used in modern vision systems.

Detecting Credit Card Fraud with Machine Learning

This project teaches practical fraud detection using machine learning, focusing on data preprocessing, model training, evaluation, and threshold optimization.

Detecting Fake Job Postings with Machine Learning

Build a machine learning system to detect fraudulent job postings using text analysis and structured metadata for robust automated screening.

Exploring Heart Disease Patterns in Clinical Data

Explore real-world patient heart health data to uncover relationships between chest pain types, demographics, and heart disease prevalence using Python analytics.

Introduction to Reinforcement Learning

Reinforcement Learning (RL) is a powerful branch of machine learning focused on training intelligent agents through interaction with their environment. In this course, you'll learn how agents gradually discover effective behaviors through trial and error. Beginning with core concepts like Markov decision processes and multi-armed bandits, you'll work your way through dynamic programming, Monte Carlo methods, and temporal difference learning.

Predicting Potable Water Quality

Build a predictive model to identify potable water using chemical measurements, evaluating model performance and feature importance for decision support.

Predicting Red Wine Quality with Machine Learning

Explore how machine learning can reveal key chemical traits that distinguish high-quality red wines using real-world data.

PyTorch Essentials

Learn the fundamental and advanced concepts needed to work with PyTorch efficiently. Gain a solid understanding tensors, including creation, operations, and reshaping. Explore the essentials of gradients, backpropagation, and linear regression before moving on to handling datasets. Master the skill needed build, train, and evaluate neural networks.

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Game Development with Unity

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Become a React Developer

Medelnivå

Excel Essentials

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Java Essentials

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Full Stack Web Development

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Frontend Development Foundations

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Mastering Data Visualization

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Machine Learning Mastery

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Become a QA Engineer

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No-Code Website Development

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Deep Learning Odyssey

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Web Development with C#

7 Kurser

293 Uppgifter

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Python from Zero to Hero

6 Kurser

195 Uppgifter

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115 Uppgifter

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101 Uppgifter

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Game Development with Unity

4 Kurser

143 Uppgifter

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4 Kurser

98 Uppgifter

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3 Kurser

39 Uppgifter

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7 Kurser

376 Uppgifter

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Full Stack Web Development

7 Kurser

343 Uppgifter

4.6

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Frontend Development Foundations

6 Kurser

306 Uppgifter

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Mastering Data Visualization

5 Kurser

145 Uppgifter

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177 Uppgifter

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125 Uppgifter

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162 Uppgifter

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75 Uppgifter

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293 Uppgifter

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Complete Social Media Management

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206 Uppgifter

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No-Code Website Development

3 Kurser

180 Uppgifter

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Deep Learning Odyssey

5 Kurser

175 Uppgifter

4.4

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Teknologier

Introduction to Neural Networks

ML Introduction with scikit-learn

Introduction to NLP

Classification with Python

In machine learning, classification is used in predictive modeling to assign input data with a class label. Sounds difficult? Don't worry! Let's cope with this! Welcome to the ML!

Cluster Analysis

Introduction to RNNs

Building a Book Recommendation System with Collaborative Filtering

Learn to build and evaluate a collaborative filtering recommender system using real-world user ratings data for personalized book recommendations.

Computer Vision Essentials

Detecting Credit Card Fraud with Machine Learning

This project teaches practical fraud detection using machine learning, focusing on data preprocessing, model training, evaluation, and threshold optimization.

Detecting Fake Job Postings with Machine Learning

Build a machine learning system to detect fraudulent job postings using text analysis and structured metadata for robust automated screening.

Exploring Heart Disease Patterns in Clinical Data

Explore real-world patient heart health data to uncover relationships between chest pain types, demographics, and heart disease prevalence using Python analytics.

Introduction to Reinforcement Learning

Predicting Potable Water Quality

Build a predictive model to identify potable water using chemical measurements, evaluating model performance and feature importance for decision support.

Predicting Red Wine Quality with Machine Learning

Explore how machine learning can reveal key chemical traits that distinguish high-quality red wines using real-world data.

PyTorch Essentials

Introduction to Neural Networks

ML Introduction with scikit-learn

Introduction to NLP

Classification with Python

In machine learning, classification is used in predictive modeling to assign input data with a class label. Sounds difficult? Don't worry! Let's cope with this! Welcome to the ML!

Cluster Analysis

Introduction to RNNs

Building a Book Recommendation System with Collaborative Filtering

Learn to build and evaluate a collaborative filtering recommender system using real-world user ratings data for personalized book recommendations.

Computer Vision Essentials

Detecting Credit Card Fraud with Machine Learning

This project teaches practical fraud detection using machine learning, focusing on data preprocessing, model training, evaluation, and threshold optimization.

Detecting Fake Job Postings with Machine Learning

Build a machine learning system to detect fraudulent job postings using text analysis and structured metadata for robust automated screening.

Exploring Heart Disease Patterns in Clinical Data

Explore real-world patient heart health data to uncover relationships between chest pain types, demographics, and heart disease prevalence using Python analytics.

Introduction to Reinforcement Learning

Predicting Potable Water Quality

Build a predictive model to identify potable water using chemical measurements, evaluating model performance and feature importance for decision support.

Predicting Red Wine Quality with Machine Learning

Explore how machine learning can reveal key chemical traits that distinguish high-quality red wines using real-world data.

PyTorch Essentials

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Mastering Data Visualization

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Video Production with Adobe

UI/UX Design Tools

Essential Office Skills

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Complete Social Media Management

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Web Development with C#

Python from Zero to Hero

SQL from Zero to Hero

С++ Essentials

Game Development with Unity

Become a React Developer

Excel Essentials

Java Essentials

Full Stack Web Development

Frontend Development Foundations

Mastering Data Visualization

Machine Learning Mastery

C++ Mastery

Java Web

Become a QA Engineer

Video Production with Adobe

UI/UX Design Tools

Essential Office Skills

Digital Marketing Essentials

Complete Social Media Management

Business AI Toolkit

No-Code Website Development

Deep Learning Odyssey

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Web Development with C#

spår

Python from Zero to Hero

spår

SQL from Zero to Hero

spår

С++ Essentials

spår

Game Development with Unity

spår

Become a React Developer

spår

Excel Essentials

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Java Essentials

spår

Full Stack Web Development

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Frontend Development Foundations

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Mastering Data Visualization

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Machine Learning Mastery

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