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Certificado
Fundamentos del Aprendizaje Automático
4.8+
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4 reseñas
Advanced
El curso ofrece una introducción a los conceptos y técnicas esenciales en el campo del aprendizaje automático. Este completo itinerario de aprendizaje abarca varios temas cruciales, como la utilización de la biblioteca scikit-learn para la iniciación al aprendizaje automático, la aplicación de la regresión lineal para el modelado predictivo, la exploración de métodos de clasificación para categorizar datos y el estudio de algoritmos de agrupación para descubrir patrones inherentes a los conjuntos de datos. Mostrar más
python
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Try Codefinity TeamsWhat you will learn
- Gain foundational insights into Machine Learning concepts.
- Explore data types and preprocessing techniques in Scikit-learn.
- Understand Machine Learning workflows including training sets and models.
- Grasp data scaling methods: StandardScaler, MinMaxScaler, MaxAbsScaler.
- Discover the structure and implementation of pipelines and ColumnTransformer.
- Develop skills in evaluating models using cross-validation, GridSearchCV, and RandomizedSearchCV.
- Assemble a complete Machine Learning pipeline integrating preprocessing and modeling.
- 109 chapters
- 13 hours
- 87 tasks
Trusted by employees of leading companies
Learning track content
Module 1 / ML Introduction with scikit-learn
Learn the Machine Learning concepts and the ML project workflow.
Preprocessing is probably the most important stage of an ML project. This chapter covers the preprocessing steps needed for almost any dataset.
- Scikit-learn ConceptsPreview
- Getting Familiar with DatasetPreview
- Dealing with Missing ValuesPreview
- Impute Missing ValuesPreview
- OrdinalEncoderPreview
- One-Hot EncoderPreview
- LabelEncoderPreview
- Encode Categorical VariablesPreview
- Why Scale the Data?Preview
- StandardScaler, MinMaxScaler, MaxAbsScalerPreview
- Scale the FeaturesPreview
- Preprocessing SummaryPreview
A pipeline is a neat way to combine all the preprocessing steps as well as a model. Pipelines make it much easier to train and use a model.
Modeling is the most fun stage of an ML project. Let's learn to build, fine-tune and evaluate the model!
- ModelsPreview
- KNeighborsClassifierPreview
- Evaluating a Model. Train-Test split.Preview
- Cross-ValidationPreview
- Evaluate the Model with Cross-ValidationPreview
- GridSearchCVPreview
- The Flaw of GridSearchCVPreview
- Tune Hyperparameters with RandomizedSearchCVPreview
- Modeling SummaryPreview
- Putting It All TogetherPreview
Module 2 / Linear Regression with Python
Let's start with the simplest Linear Regression model! You will learn the idea behind Linear Regression and how to make predictions in Python.
Most real-world prediction tasks involve more than one feature. You will learn how to handle Linear Regression with multiple features.
A straight line does not always describe the data well. Let's learn how to build a more complex model for prediction! That's what the Polynomial Regression is suited for.
Now that you know how to build many Linear Regression models, you need a way to choose the best one. This is achievable using metrics. This section explains the most used ones and the difficulties you can face using them.
Module 4 / Ensemble Learning
What is an ensemble? How are ensembles different from standard machine-learning models? What are the types of ensembles? Let's consider the answers to these questions.
Let's consider some commonly used bagging ensemble models, the features of their use, and also apply some of them to solve real-life tasks.
The mechanism of work of boosting models differs from bagging models. Now we will explore these distinctions, gain insights into utilizing model boosting for problem-solving, and illustrate its functionality through practical demonstrations.
Let's consider some commonly used stacking ensemble models, the features of their use, and also apply some of them to solve real-life tasks.
Module 5 / Logistic Regression Mastering
In this project, we are going to understand Logistic Regression.
Requirements
- A computer with a browser - all browsers are supported.
- Your enthusiasm to enhance your tech skills.
- Everything else needed to start learning and practicing is already included in this course.
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Thanks to them I'm learning a lot faster because they help you to understand everything from scratch. It's the best website that helps people with no background in IT...
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it's simply perfectly well explained! so far I have not experienced any difficulty because everything is so well managed...
Ruslan Kravchuk
The main thing is to learn and not give up
The material is good, there is a lot to learn, all in order to become better and the main thing is to learn what you want....
Matteo Comune
Thanks to them I'm learning a lot…
Thanks to them I'm learning a lot faster because they help you to understand everything from scratch. It's the best website that helps people with no background in IT...
Yuliana Cadavid
great course for beginners
great course for beginners, they test your knowledge in every lesson...
Elpunzon
I am enjoying my Codefinity experience…
I am enjoying my Codefinity experience learning Python. The self-paced way of learning is great because I can fit it into my schedule...
Alexandru Alexandru
Is nice to learn from codefinity
Is nice to learn from codefinity. Its easy and have good examples on what I learned here...
jacob Templet
Easy to follow along with and provides…
Easy to follow along with and provides challenge in my every day life. The challenge keeps me wanting to learn day after day...
Elan
Codefinity is a comprehensive learning…
Codefinity is a comprehensive learning tool to help you develop your skills as a software engineer or data scientist. The exercises are fun and a good way to sharpen your skills...
Thibault
First time learning how to code
First time learning how to code and successfully doing so with codefinity - thank you...
Adrien Morel
Well designed for total beginners
Well designed for total beginners, incremental progress and makes me feel confident....
_Gracy
it's simply perfectly well explained
it's simply perfectly well explained! so far I have not experienced any difficulty because everything is so well managed...
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