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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

Explore the fundamentals of Natural Language Processing (NLP) by learning essential text preprocessing techniques and methods for representing text data. Gain practical experience with the tools used to clean, analyze, and interpret textual information. Develop the skills needed to transform raw language into structured insights, laying a strong foundation for advanced applications in artificial intelligence and machine learning.

Classification with Python

Master the core classification algorithms that power modern machine learning. Explore how models like k-NN, logistic regression, decision trees, and random forests make predictions, evaluate their accuracy, and understand when to use each. Build the skills to compare models and choose the best one for your data.

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.

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.

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.

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.

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.

Tecnologie

Tema

Livello

Tipo di Lezione

Carriere

Machine Learning Mastery

Deep Learning Odyssey

Corsi e Progetti

Carriere

Machine Learning Mastery

Deep Learning Odyssey

traccia

Machine Learning Mastery

traccia

Deep Learning Odyssey

Corsi e Progetti

Tecnologie