Machine Learning e Data Mining in R
This program provides a pragmatic introduction to machine learning and data mining using R, encompassing fundamental techniques and tackling significant data analysis difficulties.
Description for Machine Learning e Data Mining in R
-
Data Manipulation and Visualization With R: Acquire skills in importing, manipulating, and visualizing data utilizing R and the tidyverse packages, including dplyr and ggplot2.
-
Supervised and Unsupervised Learning in R: Acquire the expertise to identify and address supervised and unsupervised learning challenges utilizing R packages such as leaps, glmnet, and pls.
-
Comprehending Shallow and Deep Neural Networks: Understand the distinctions between shallow and deep artificial neural networks, crucial for addressing various machine learning challenges.
-
Confronting Data Obstacles: Comprehend the resolution of persistent data challenges like as collinearity, overfitting, regularization, and knowledge transfer.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On Coursera provided by University di Napoli Federico II
Duration: 3 weeks at 10 hours a week
Schedule: Flexible
Pricing for Machine Learning e Data Mining in R
Use Cases for Machine Learning e Data Mining in R
FAQs for Machine Learning e Data Mining in R
Reviews for Machine Learning e Data Mining in R
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Machine Learning e Data Mining in R
This course equips students with the necessary business leadership skills and technical knowledge to propel the success of ML.
By learning how to analyze health data and sequence genomes using AI, this course equips students with the tools they need to contribute to medical research.
The objective of this course is to provide students with an understanding of the future of finance and investments, as well as the role of emergent AI and Machine Learning technologies in InsurTech and Real Estate Tech.
The purpose of this course is to provide students with the opportunity to develop practical, cloud-based machine learning skills. It focuses on the use of Apache Spark to teach logistic regression modeling on Google Cloud.
With the help of machine learning, this course teaches students how to predict health insurance costs by taking into account factors like age, gender, BMI, and smoking habits.
In order to facilitate effective learning, this course provides learners with the necessary skills to develop scalable and resilient ML solutions on AWS, combining theory and practical experience.
With an emphasis on quantitative, pairs, and momentum trading, this course prepares students to create and backtest sophisticated trading strategies utilizing machine learning.
Learners will gain the fundamentals necessary to implement AI solutions on Microsoft Azure with this course specialization, which will set them up for success with the AI-900 competency.
Using the complete machine learning pipeline in computer vision, this course teaches students how to use MATLAB for object detection and classification in images.
This course�trains on source code summary and programming language identification with Vertex AI LLM within Google Cloud.
Featured Tools
The course's main objectives are to deploy solutions using Vertex AI and integrate machine learning into Google Cloud data pipelines, such as AutoML and BigQuery ML.
Learn through case studies, techniques, challenges, and objectives to master classification tasks, techniques, and metrics in Python for effective machine learning on various datasets.
The topics of this AI course include the optimization of policies in reinforcement learning, the utilization of dimensionality reduction in unsupervised learning, and the classification and definition of constraints in supervised learning.
This course teaches aspiring data scientists to train and compare classification models using supervised machine learning techniques, focusing on practical applications and best practices.
Learn to create responsive websites using HTML, CSS, JavaScript, and React, utilize the Bootstrap framework, collaborate with GitHub, and prepare for coding interviews with portfolio-ready projects.