APPLIED MACHINE LEARNING METHODS (USING PYTHON)

Applied Machine Learning Methods using Python
04
Abr

This course is designed for data professionals in intermediate level who are interested in building machine learning algorithms and predictive models with Python in real data analysis projects.

Course Objectives

  • To improve your knowledge on machine learning and AI
  • To build the robust predictive analytics models
  • To improve the accuracy of machine learning models
  • To know how to choose the most related Machine Learning model in real projects
Inclduing a capstone Machine Learning and predictive models project on your real data

Target Audience

  • IT professionals
  • Computer Science and IT Students
  • Data Scientists
  • Data Analysts
  • Technical managers

Course Syllabus

  • Fundamentals of Machine Learning
  • Machine Learning pipeline
  • Underfitting, Overfitting and Generalization
  • Data preprocessing and visualization
  • Machine Learning – Statistics essentials
  • Supervised and unsupervised algorithms
  • All Regression algorithms
  • Decision Tree and ensembling methods
  • K-Nearest Neighbors (K-NN)
  • Support Vector Machine (SVM)
  • Naive Bayes
  • K-Means Clustering
  • Hierarchical Clustering
  • Dimensionality Reduction
  • Bias vs Variance Tradeoff
  • Model Evaluation and Performance
  • Introduction to Advanced Machine Learning –Reinforcement Learning and Deep Learning

Requirements

  • Basic knowledge of Python
  • Basic knowledge of descriptive statistics and mathematics

Código da Formação: CRS-L1-004

Preço: Sob consulta

Duração: 32 horas

Quer uma formação à medida para a sua empresa?

  • To improve your knowledge on machine learning and AI
  • To build the robust predictive analytics models
  • To improve the accuracy of machine learning models
  • To know how to choose the most related Machine Learning model in real projects
Inclduing a capstone Machine Learning and predictive models project on your real data
  • IT professionals
  • Computer Science and IT Students
  • Data Scientists
  • Data Analysts
  • Technical managers
  • Fundamentals of Machine Learning
  • Machine Learning pipeline
  • Underfitting, Overfitting and Generalization
  • Data preprocessing and visualization
  • Machine Learning – Statistics essentials
  • Supervised and unsupervised algorithms
  • All Regression algorithms
  • Decision Tree and ensembling methods
  • K-Nearest Neighbors (K-NN)
  • Support Vector Machine (SVM)
  • Naive Bayes
  • K-Means Clustering
  • Hierarchical Clustering
  • Dimensionality Reduction
  • Bias vs Variance Tradeoff
  • Model Evaluation and Performance
  • Introduction to Advanced Machine Learning –Reinforcement Learning and Deep Learning
  • Basic knowledge of Python
  • Basic knowledge of descriptive statistics and mathematics

Course Content

Time: 32 hours

Curriculum is empty

Instructor

Free
Duração :32 hour
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