Applied Machine Learning Methods (Using Python)
Description
This course is designed for data professionals in intermediate level who are interested to build 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
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 Trees 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
Registration fee :
€ 590
Target audience
- IT professionals
- Computer Science and IT Students
- Data Scientists
- Data Analysts
- Technical managers
Requirements
- Basic knowledge of Python Basic knowledge of descriptive statistics and mathematics