The goal of machine learning is to program computers to use sample data or past experiences to solve a certain obstacle.. Many successful machine learning applications already exist, including systems that analyze past sales data to predict customer behavior, take full advantage of robot behavior so that a task can be completed with minimal resources and extract knowledge from bioinformatics data.
Then, some of the resources to help you get started learning machine learning concepts along with their practical applications are shown.
Blogs / Means
1. Step-by-step learning path on machine learning
This is an ideal resource for you to master machine learning. This learning path provides you with a step-by-step method to become a master in machine learning.. Once you've covered the basics of machine learning, can move on to higher level concepts, What deep learningDeep learning, A subdiscipline of artificial intelligence, relies on artificial neural networks to analyze and process large volumes of data. This technique allows machines to learn patterns and perform complex tasks, such as speech recognition and computer vision. Its ability to continuously improve as more data is provided to it makes it a key tool in various industries, from health..., red neuronalNeural networks are computational models inspired by the functioning of the human brain. They use structures known as artificial neurons to process and learn from data. These networks are fundamental in the field of artificial intelligence, enabling significant advancements in tasks such as image recognition, Natural Language Processing and Time Series Prediction, among others. Their ability to learn complex patterns makes them powerful tools...
2. Essentials of machine learning algorithms
With this, You can dive into the essential components of machine learning, which includes algorithms / techniques used in machine learning. In this post, the algorithms have been explained in the simplest possible way using interesting real life examples.
3. Best YouTube Videos on Machine Learning, deep learning and neural networks
More to read, sometimes video tutorials can help you learn concepts quickly. Here's a great collection of the best YouTube videos available on machine learning, deep learning and neural networks. These videos include talks and comprehensive tutorials that teach various aspects of machine learning..
Resources by Machine Learning Category
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Exploration / data preprocessing:
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Machine learning algorithms:
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Impulse and set methods:
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Improve model performance:
Books
1. Pattern recognition and machine learning
This book is best suited for beginners with no prior knowledge of machine learning and pattern accreditation. Provides a comprehensive introduction to the field of pattern accreditation and machine learning.
2. Elements of statistical learning
This book is highly recommended by data science experts. Covers all the necessary algorithms you need to master machine learning concepts. This book describes important ideas that cover a wide range of topics, from the supervised learningSupervised learning is a machine learning approach where a model is trained using a set of labeled data. Each input in the dataset is associated with a known output, allowing the model to learn to predict outcomes for new inputs. This method is widely used in applications such as image classification, speech recognition and trend prediction, highlighting its importance in... hasta el no supervisado.
3. Bayesian reasoning and machine learning
This is another book that covers important aspects of Bayesian reasoning with the elementary to advanced level of machine learning concepts..
4. Machine learning: a probabilistic perspective
This book is an excellent starting point for data science beginners.. This book presents intuitive examples that are fun to read and help you understand complex concepts in a simplistic way.. The books cover a wide range of topics, from probability and statistics arguments to advanced machine learning concepts.
5. Information theory, inference and learning algorithms
This book is intended for people interested in mastering advanced machine learning concepts, that include data compression, noisy channel encoding, probabilities and inferences, neural networks, sparse graphics codes, etc.
