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DEEP 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 and Deep Learning. Once a domain of researchers and PhDs only, Machine Learning and Deep 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 and Deep 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 Deep Learning skills by employers -- and the job salaries of Machine Learning and Deep Learning practitioners -- are only bound to increase over time, as AI becomes more pervasive in society. Machine Learning and Deep Learning are a future-proof career.

• Throughout this program you will practice your Machine Learning and Deep 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 Deep 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 and Deep Learning.

• In this Diploma, you practice with real-life examples of Machine learning/Deep learning and see how it affects society in ways you may not have guessed!

• Length of Program: 140 Hrs.

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• Courses Included in this Diploma: o 1- Machine Learning with Python (100 Hours) o 2- Deep Learning (40 Hours)

• 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.

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• Delivery Options o 1- Classroom Live o 2- Virtual Classroom Online live o 3- Onsite Training

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: Predicting car Miles Per Gallon usage

• Project 4: Finding Donors for Charity

• Project 5: Face-Recognition

• project 6: Dog Breed Recognition

• Project 7: Self-Driving Cars

• Project 8: Time Series Analysis

• Project 9: Social media analytics

• Project 10: Generate Faces

• Project 11: Cancer Diagnosis

• Project 12: Speech recognition

• Project 13: Optical Character Recognition (OCR)

• Project 14: freelance Projects (Kaggle Competitions)

Capstone projects in many fields

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

• 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

• Web APP [FLASK / DJANGO]

• Intro to data science

o Database with SQLite

o Numpy and matrix operations

o Pandas

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o Data visualization

o Git command line and GUI based

o Web Scraping for data collecting (Beautiful Soup)

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

• Basic 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

• Advanced Algorithms

o Greedy Algorithms

o Genetic Algorithms

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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 Linear Algebra and Statistics for ML

o setup google Colab [Cloud Computing]

• 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

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o K-nearest neighbor classifier

o Support vector machine (SVM)

o Decision tree classifier

o Ensemble learning

o Classification Evaluation Metrics

o Bagging and Boosting

• Clustering Problems

o Dimensionality reduction

o K-means

o DBSCAN

o hierarchical clustering

o Association Rules

• Reinforcement learning

o Q-Learning

• Model Selection and evaluation

o Loss functions

o Gradient descent

o Bias-variance tradeoff

o Cross-validation

o Hyperparameter tuning

• Result communication and report

IV- Deep Learning Topics

• Neural Networks

o INTRODUCTION TO NEURAL NETWORKS

o IMPLEMENTING GRADIENT DESCENT

o TRAINING NEURAL NETWORKS

o SENTIMENT ANALYSIS

o DEEP LEARNING WITH PYTORCH

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• Convolutional Neural Networks

o INVARIANCE, STABILITY

o CLOUD COMPUTING

o CONVOLUTIONAL NEURAL NETWORK

o CNNS IN PYTORCH

o PROPERTIES OF CNN REPRESENTATIONS: INVERTIBILITY,

STABILITY, INVARIANCE.

o WEIGHT INITIALIZATION

o AUTOENCODERS

o VARIATIONAL AUTOENCODERS

o DEEP LEARNING FOR CANCER DETECTION

o VARIABILITY MODELS (DEFORMATION MODEL, STOCHASTIC

MODEL).

o SCATTERING NETWORKS

o GROUP FORMALISM

o SUPERVISED LEARNING: CLASSIFICATION.

o COVARIANCE/INVARIANCE: CAPSULES AND RELATED

MODELS.

o CONNECTIONS WITH OTHER MODELS: DICTIONARY

LEARNING, LISTA.

o OTHER TASKS: LOCALIZATION, REGRESSION.

o EMBEDDINGS (DRLIM), INVERSE PROBLEMS

o EXTENSIONS TO NON-EUCLIDEAN DOMAINS

o DYNAMICAL SYSTEMS: RNNS.

• Recurrent Neural Networks

o RECURRENT NEURAL NETWORKS

o LONG SHORT-TERM MEMORY NETWORK

o IMPLEMENTATION OF RNN & LSTM

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o HYPERPARAMETERS

o EMBEDDINGS & WORD2VEC

o SENTIMENT PREDICTION RNN

• Generative Adversarial Networks

o GENERATIVE ADVERSARIAL NETWORK

o MAXIMUM ENTROPY DISTRIBUTIONS

o DEEP CONVOLUTIONAL GANs

o PIX2PIX & CYCLEGAN

• Model Deployment

o INTRODUCTION TO DEPLOYMENT

o DEPLOY A MODEL

o CUSTOM MODELS & WEBHOSTING

o MODEL MONITORING

o UPDATING A MODEL

• MISCELLANEOUS TOPICS

o NON-CONVEX OPTIMIZATION FOR DEEP NETWORKS

o STOCHASTIC OPTIMIZATION

o ATTENTION AND MEMORY MODELS

o OPEN PROBLEMS

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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 ابسلون للتدريب

• 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

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