Learn the basics of writing and running Python scripts to more advanced features such as file operations, regular expressions, working with binary data, and using the extensive functionality of Python modules.
Perquisites: Basic skills with at least one programming language are desirable.
introduction and syntax
data types and operations
Object-Oriented Programming (OOP)
Multithreading and multiprocessing
Sockets and APIs
Building your own server
Numpy and matrix operations
Pandas and data handling
Git command line and GUI based
Web Scraping for data collecting
Machine Learning with Python
Machine learning is changing the world and if you want to be a part of the ML revolution, this is a great place to start!
In this course, we will be reviewing two main components:
First, you will be learning about the purpose of Machine Learning and where it applies in the real world.
Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation and Machine Learning algorithms.
In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed!
Who is this class for:
This course is primarily for individuals who are passionate about the field of data science and who are aspiring to apply machine learning in their business, industry or research.
Program Duration: 50 hours
Program Language: English / Arabic
Location: EPSILON TRAINING CENTER | Head Office
Participants will be granted a completion certificate from Epsilon Training Institute, USA if they attend a minimum of 80 percent of the direct contact hours of the Program and after fulfilling program requirements (passing both Final Exam and Project to obtain the Certificate)
Introduction to ML and Business cases
The difference between ML, Big data, Data analysis and Deep Learning
Linear Algebra and Statistics for ML
K-nearest neighbour regression
Decision tree regression
Regression Evaluation Metrics
K-nearest neighbour classifier
Support vector machine (SVM)
Decision tree classifier
Classification Evaluation Metrics
Model Selection and evaluation
Result communication and report
|Model Selection and evaluation|
|Result communication and report|