This document provides information about a Machine Learning Specialist training program. The 100-hour program uses instructor-led training and hands-on projects to teach Python, machine learning algorithms, and their real-world applications. The goal is to equip students with in-demand skills for a career in applied machine learning. The curriculum covers topics like data analysis, predictive modeling, feature engineering, and deep learning architectures. Students will complete projects such as image classification and optica character recognition to build an employment portfolio.

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MACHINE LEARNING SPECIALIST
1) Learning Methodology
• Instructor-Led Classroom Training (ILT).
2) Prerequisites:
• Basic skills with at least one programming language are desirable. (Optional)
3) Training Program Description:
• AI is revolutionizing the way we live, work and communicate. At the heart of AI is
Machine Learning. Once a domain of researchers and PhDs only, Machine
Learning has now gone mainstream thanks to its practical applications and
availability in terms of consumable technology and affordable hardware.
• The demand for Machine Learning professionals is booming, far exceeding the
supply of personnel skilled in this field. The industry is clearly embracing AI,
embedding it within its fabric. The demand for Machine Learning skills by
employers -- and the job salaries of Machine Learning practitioners -- are only
bound to increase over time, as AI becomes more pervasive in society. Machine
Learning are a future-proof career.
• Throughout this program you will practice your Machine Learning skills through a
series of hands-on labs, assignments, and projects inspired by real world
problems and data sets from the industry. You’ll also complete the program by
preparing a Machine Learning capstone project that will showcase your applied
skills to prospective employers.
• This program is intended to prepare learners and equip them with skills required
to become successful AI practitioners and start a career in applied Machine
Learning.
• 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!
• Length of Program: 100 Hrs.
• Courses Included in this Diploma:
o 1- Python for data science (40 Hours)
o 2- Machine Learning (60 Hours)

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• Who is this class for?
o This Diploma is primarily for individuals who are passionate about the field
of data science and data analysts and who are aspiring to apply machine
learning and Deep Learning in their business, industry or research.
o Developers and Software Engineers
o Analytics Managers and Professionals
o Statisticians with an interest in Machine and deep learning
• What you will learn
o Use Python and SQL to access and analyze data from several different
data sources.
o Build predictive models using a variety of unsupervised and supervised
machine learning techniques.
o Perform feature engineering to improve the performance of machine
learning models.
o Optimize, tune, and improve algorithms according to specific metrics like
accuracy and speed.
o Compare the performances of learned models using suitable metrics.
o concepts of Machine Learning and Deep Learning, including various
Neural Networks for supervised and unsupervised learning.
o Use of popular Machine Learning and Deep Learning libraries such as
Keras, PyTorch, and Tensorflow applied to industry problems.
o Build, train, and deploy different types of Deep Architectures, including
Convolutional Networks, Recurrent Networks, and Autoencoders.
o Application of Machine Learning and Deep Learning to real-world
scenarios such as object recognition and Computer Vision, image and
video processing, text analytics, Natural Language Processing,
recommender systems, and other types of classifiers.
o Master Deep Learning at scale with accelerated hardware and GPUs.
• Delivery Options
o 1- Classroom Live
o 2- Virtual Classroom Online live
o 3- Onsite Training

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4) program outcomes:
• Use Python and SQL to access and analyze data from several different data
sources.
• Build predictive models using a variety of unsupervised and supervised machine
learning techniques.
• Perform feature engineering to improve the performance of machine learning
models.
• Optimize, tune, and improve algorithms according to specific metrics like
accuracy and speed.
• Compare the performances of learned models using suitable metrics.
• deep learning advanced architectures
• Building computer vision Models
• Understanding image recognition and image processing techniques
• Introduction in natural language processing

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5) Projects
This program is comprised of many career-oriented projects. Each project you build will
be an opportunity to demonstrate what you’ve learned in the lessons. Your completed
projects will become part of a career portfolio that will demonstrate to potential
employers that you have skills in data analysis and feature engineering, machine
learning algorithms, and training and evaluating models.
One of our main goals at ETI is to help you create a job-ready portfolio of completed
projects. Building a project is one of the best ways to test the skills you’ve acquired and
to demonstrate your newfound abilities to future employers or colleagues. Throughout
this program, you’ll have the opportunity to prove your skills by building the following
projects
Building a project is one of the best ways both to test the skills you've acquired and to
demonstrate your newfound abilities to future employers. Throughout this program,
you'll have the opportunity to prove your skills by building the following projects:
• Project 1: Exploring the Titanic Survival Data
• Project 2: Predicting Housing Prices
• Project 3: Finding Donors for Charity
• project 4: Dog Breed Recognition
• Project 5: Customer segments
• Project 6: Image Classification
• Project 7: Optical Character Recognition (OCR)
• Project 8: freelance Projects (Kaggle Competitions)
• Capstone project
1- Self-driving cars 2- Trading
2- Business 4- Computer vision

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6) Training Program Curriculum:
I- Python 3 Topics
• Introduction
o syntax
o data types and operations
o I/O
o Operators and bitwise
o Lists
o Tuples
o If statements
o For – while loops
• Intro to Object-Oriented Programming (OOP)
o Special Functions
o Strings
o Classes
o Inheritance
o Regular expressions
o Working with files
o Python generators
o Python Decorators
o Exceptions
o Regular expressions
• Intro to data science
o Database with SQLite
o Numpy and matrix operations
o Pandas
o Data visualization
o Git command line
o Web Scraping for data collecting

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II- Data Structures & Algorithms Topics
• Introduction
o How to Solve Problems
o Big O Notation
• Data Structures
o Collection data structures (lists, arrays, linked lists, queues,
stack)
o Recursion
o Trees
o Maps and Hashing
• Algorithms
o Binary Search
o Sorting Algorithms
o Divide & Conquer Algorithms
o Maps and Hashing
o Practice Problems: Randomized Binary Search, K-smallest
elements using Heaps, Build Red-Black Tree, bubble sort,
merge sort, quick sort, sorting strings, Linear-time median
finding

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III- Machine Learning Topics
• Linear algebra
• Calculus
• Statistics
• Introduction to ML and Business cases
o The difference between ML, Big data, Data analysis and
Deep Learning
o Cloud Computing (Google Colab)
• Data preprocessing
o Importing libraries
o Data acquisition
o Data cleaning
o Handling missing data
o Categorical data
o Data splitting
o Feature scaling
o Feature Engineering
• Regression problem
o Linear Regression
o Multi-linear regression
o Polynomial regression
o K-nearest neighbor regression
o Decision tree regression
o Regression Evaluation Metrics
• Classification problem
o Logistic Regression
o Naive Bayes
o K-nearest neighbor classifier

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o Support vector machine (SVM)
o Decision tree classifier
o Ensemble learning
o Classification Evaluation Metrics
• Clustering Problems
o Dimensionality reduction
o K-means
o hierarchical clustering
• Model Selection and evaluation
o Loss functions
o Gradient descent
o Bias-variance tradeoff
o Cross-validation
o Hyperparameter tuning
• Result communication and report

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FOR MORE INFORMATION:
Website: https://epsiloneg.com
E-mail: info@epsiloneg.com
Mobile: +2 01122885566 / +2 01011933233 / +20 2 22749985
Address: Elserag Shopping Mall, Residential Building 1,
Entrance 1, Makram Ebeid, Nasr City, cairo, Egypt
Contact US
To get more details Regarding
special discount for groups.

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CERTIFICATE
• Participants will be granted a completion certificate from Epsilon Training
Institute, Delaware, 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)
REGISTRATION PROCEDURES
• Confirmation of registration is based on receipt of a Purchase Order or
Registration Form.
• Training Program registrations will not be confirmed until registration is complete
and billing information is received in full
PAYMENT TERMS AND METHODS
• Payment must be made prior to course commencement at Epsilon Training
Center, Nasr City HQ
• In-Person
o In Cash to our address: Elserag shopping mall,
Residential Building 1, Entrance 1, Floor 11
o By cheque - Payable to: Epsilon ‫للتدريب‬ ‫ابسلون‬
• Bank transfer to our ACC in (Exculding Bank Transfer Fees) :
QNB ALAHLI Acc /20318280579-69 EGP Branch code / 00078
• Vodafone Cash to 01011933233

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Get in Touch
Egypt USA
Location: Elserag Shopping Mall,
Residential Building 1,
Makram Ebeid, Nasr City,
cairo 11762
Location: 919 N Market St, Wilmington, DE 19801
Telephone: +2 (011) 2288-5566 / +2 (010)
1193-3233 / +2 02 2274 9985
Telephone: +1 (408) 641-4068 / +1(415) 683-7459
+1 (415) 877-6750 / +1 (917) 472-1201
Website: https://epsiloneg.com :Website https://epsilonti.org
Email: info@epsiloneg.com Email: info@epsilonti.org
CR# 118268 TAX# 672-411-008 CR# 7078427 TAX# 38-4095665