Machine learning is the process of automatically using statistical techniques to analyze information and make predictions about the future..
In other words, is the use of statistical techniques to analyze information and make decisions based on them.
The machine learning process has a wide range of applications, from simple tasks such as predicting the relevance of a document during indexing in a search engine, even more complex tasks like self-driving cars.
Machine learning can be used for a wide variety of more everyday things, how to identify spam in email, recommend which product you might like or predict disease. Machine learning is also the foundation of artificial intelligence.
Python is a well-known programming language for scientific and engineering applications. With the rise of machine learning algorithms, there has been a growing demand in the data science community to use Python for machine learning.
Machine Learning en Python Bootcamp con 5 proyectos capstone
Master algorithms and machine learning models in Python with hands-on projects in data science. Code workbooks included.
Who is this course for?
- Anyone who's already started their data science journey and now wants to master machine learning.
- This course is aimed at both beginners and intermediate machine learning.
- For this course to make sense, must know linear algebra well, the calculation, statistics, probability and Python programming language.
What you will learn
- Theory and practical implementation of linear regression using sklearn
- Theory and practical implementation of logistic regression using sklearn
- Selection of functions using RFECV
- Data transformation with linear and logistic regression.
- Evaluation metrics to analyze the performance of the models
- Industrial relevance of linear and logistic regression
- Math behind KNN algorithms, SVM e Naive Bayes
- Implementation of KNN, SVM y Naive Bayes usando sklearn
- Attribute selection methods: Gini index and entropy
- Math Behind Decision Trees and Random Forest
- Increased algorithms: Adaboost, Gradient Boosting y XgBoost
- Different algorithms for clustering
- Different methods for dealing with unbalanced data
- Correlation filtering
- Variance filtering
- PCA and LDA
- Collaborative and content-based filtering
- Singular decomposition value
- Different algorithms used for the prediction of time series
This course is free thanks to a coupon that you can find below.
Take into account that these types of coupons last for a very short time.
If the coupon has already expired you can purchase the course with a great discount.
The estimated coupon end date is for the day 1-2 of May, but it can beat at any time.
To obtain the course with your coupon, click on the following button:
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