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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.

 

Training Topics:

introduction and syntax

data types and operations

I/O

Object-Oriented Programming (OOP)

Special Functions

Exceptions

Regular expressions

Multithreading and multiprocessing

Sockets and APIs

Database

Building your own server

Numpy and matrix operations

Pandas and data handling

Data visualization

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 Diploma, 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: 90 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)

 

COURSE CONTENTS

Training Topics:

Machine Learning

Introduction to ML and Business cases

The difference between ML, Big data, Data analysis and Deep Learning

Linear Algebra and Statistics for ML

Data preprocessing

Regression problem

Linear Regression

Multi-linear regression

Polynomial regression

K-nearest neighbour regression

Decision tree regression

Regression Evaluation Metrics

Classification problem 

Logistic Regression

Naive Bayes

K-nearest neighbour classifier

Support vector machine (SVM)

Decision tree classifier

Ensemble learning

Classification Evaluation Metrics

Model Selection and evaluation

Loss functions

Gradient descent

Bias-variance tradeoff

Cross-validation

Hyperparameter tuning

Clustering Problems

Dimensionality reduction

K-means

DBSCAN

hierarchical clustering

Association Rules

Result communication and report

 

 

 

Course Curriculum

Machine Learning
Regression problem
Classification problem
Model Selection and evaluation
Clustering Problems
Result communication and report

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