Description
Python has also become the dominant language in machine learning and data science. Python is widely used to fit complex models to scattered datasets.
Machine learning is a scientific field of study in which various techniques are developed to try to make computers learn in a similar way to humans.
In programming terms, machine learning is just a few lines of code. With this course you will be able to understand the theory, logic, model setup and optimization. The training covers regression and classification models, including tree-based methods, clustering and sparse regression models.
In addition, modern data science and machine learning concepts and models are discussed with the basics of programming and statistics; it is aimed to gain practicality with applications and examples.
About Education
Education Objectives:
- Learning Python and Data Science Libraries,
- To be able to do Statistical and Exploratory Data Analysis,
- Regression and Classification Problems
- Understand basic Machine Learning Algorithms,
- Understanding Unsupervised Learning,
- To be able to answer the question “How to build and quantitatively evaluate various models appropriate to a set of problems?”,
- Understand the importance of data pre-processing and organization,
- Confident, able to benchmark the effectiveness of their models using a rigorous training and testing framework,
- To be able to answer the question “How do various types of models work?”,
- Learn some modern, state-of-the-art machine learning techniques.
Training Content:
- Installations
- Python Basics
- Basic Machine Learning Algorithms
Classification with KNN
Regression with KNN
Decision Trees
Random Forests
Classification with Random Forests
Regression with Random Forests
Decision Support Machines
Classification with Decision Support Machines
Regression with Decision Support Machines
Gradient Boosting
Classification with Gradient Boosting
Regression with Gradient Boosting
- Introducing Machine Learning (ML)
Introduction to machine learning
Introduction to related packages in Python (Numpy, Scipy and SciKit-Learn)
- Data Reprocessing
Learn the why and how of preprocessing your data with scaling transformations and coding
Typical standardization and normalization procedures
- Introduction to Modeling
How to move from a linear regression and statistical model to a machine learning model?
Introduction to modeling techniques
- Model Evaluation
Measuring Modeling Effectiveness (Verification and testing; techniques such as cross validation)
Criteria that can be used to judge the model and the different criteria that are appropriate for evaluation
- Regulation
Techniques for performing feature selection
- Clustering
Unsupervised learning technique to uncover patterns and structure in data
- Advanced Techniques
Some more advanced model fitting using algorithms such as gradient boosted trees and support vector machines
- Unsupervised Learning
What is Unsupervised Learning?
Kmeans
Spectral Clustering
Mean-shift
Affinity Propagation
How to Measure the Performance of Clustering Algorithms?
Who Should Receive the Training?
- People interested in data science, artificial intelligence and machine learning.
Requirements
- To be able to use Python programming language.
- To be able to perceive or have knowledge of data structures.
- Be familiar with common statistical terms.
- To have completed Python basic education level.
Plan this training institutionally!
This training can be planned in different durations and content specific to your organization. Please contact us for detailed rich content and planning to realize your training objectives.
