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Prerequisites:

  • Basic skills with at least one programming language are desirable.

 

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 and Deep Learning and see how it affects society in ways you may not have guessed!

What you will learn

  • 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.
  • concepts of Machine Learning and Deep Learning, including various Neural Networks for supervised and unsupervised learning.
  • Use of popular Machine Learning and Deep Learning libraries such as Keras, PyTorch, and Tensorflow applied to industry problems.
  • Build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
  • 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.
  • Master Deep Learning at scale with accelerated hardware and GPUs.

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: Face-Recognition
      • Project 9: Generate Faces
      • Project 10: Time Series Analysis
      • Project 11: Cancer Diagnosis
      • Project 12: freelance Projects (Kaggle Competitions)

Capstone projects in many fields:

  • Self-driving cars
  • Business
  • Trading
  • Computer vision

 

Program Duration: 130 hours

Program Language: English / Arabic

Location: EPSILON TRAINING INSTITUTE | 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

I- Python 3 Topics

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

 

II- Data Structures & Algorithms Topics

  • Introduction
    • How to Solve Problems
    • Big O Notation
  • Data Structures
    • Collection data structures (lists, arrays, linked lists, queues, stack)
    • Recursion
    • Trees
    • Maps and Hashing
  • Algorithms
    • Binary Search
    • Sorting Algorithms
    • Divide & Conquer Algorithms
    • Maps and Hashing
    • 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

 

 

 

 

 

 

 

III- Machine Learning Topics

  • Linear algebra
  • Calculus
  • Statistics
  • Introduction to ML and Business cases
    • The difference between ML, Big data, Data Analysis and Deep Learning
    • Cloud Computing (Google Colab)
  • Data preprocessing
    • Importing libraries
    • Data acquisition
    • Data cleaning
    • Handling missing data
    • Categorical data
    • Data splitting
    • Feature scaling
    • Feature Engineering
  • Regression problem
    • Linear Regression
    • Multi-linear regression
    • Polynomial regression
    • K-nearest neighbor regression
    • Decision tree regression
    • Regression Evaluation Metrics
  • Classification problem
    • Logistic Regression
    • Naive Bayes
    • K-nearest neighbor classifier
    • Support vector machine (SVM)
    • Decision tree classifier
    • Ensemble learning
    • Classification Evaluation Metrics
  • Clustering Problems
    • Dimensionality reduction
    • K-means
    • hierarchical clustering
  • Model Selection and evaluation
    • Loss functions
    • Gradient descent
    • Bias-variance tradeoff
    • Cross-validation
    • Hyperparameter tuning
  • Result communication and report

 

IV- Deep Learning Topics

 

  • Neural Networks
    • INTRODUCTION TO NEURAL NETWORKS
    • IMPLEMENTING GRADIENT DESCENT
    • TRAINING NEURAL NETWORKS
  • Convolutional Neural Networks
    • INVARIANCE, STABILITY
    • CLOUD COMPUTING
    • CONVOLUTIONAL NEURAL NETWORK
    • PROPERTIES OF CNN REPRESENTATIONS: INVERTIBILITY, STABILITY, INVARIANCE.
    • WEIGHT INITIALIZATION
    • AUTOENCODERS
    • VARIATIONAL AUTOENCODERS
    • VARIABILITY MODELS (DEFORMATION MODEL, STOCHASTIC MODEL).
    • SCATTERING NETWORKS
    • GROUP FORMALISM
    • SUPERVISED LEARNING: CLASSIFICATION.
    • COVARIANCE/INVARIANCE: CAPSULES AND RELATED MODELS.
    • CONNECTIONS WITH OTHER MODELS: DICTIONARY LEARNING, LISTA.
    • OTHER TASKS: LOCALIZATION, REGRESSION.
    • EMBEDDINGS (DRLIM), INVERSE PROBLEMS
    • EXTENSIONS TO NON-EUCLIDEAN DOMAINS
    • DYNAMICAL SYSTEMS: RNNS.
  • Recurrent Neural Networks
    • RECURRENT NEURAL NETWORKS
    • LONG SHORT-TERM MEMORY NETWORK
    • IMPLEMENTATION OF RNN & LSTM
    • HYPERPARAMETERS
    • EMBEDDINGS & WORD2VEC
  • Generative Adversarial Networks
    • GENERATIVE ADVERSARIAL NETWORK
    • MAXIMUM ENTROPY DISTRIBUTIONS
    • DEEP CONVOLUTIONAL GANs
    • PIX2PIX & CYCLEGAN
  • Model Deployment
    • INTRODUCTION TO DEPLOYMENT
    • DEPLOY A MODEL
    • CUSTOM MODELS & WEBHOSTING
    • MODEL MONITORING
    • UPDATING A MODEL
  • MISCELLANEOUS TOPICS
    • NON-CONVEX OPTIMIZATION FOR DEEP NETWORKS
    • STOCHASTIC OPTIMIZATION
    • ATTENTION AND MEMORY MODELS
    • OPEN PROBLEMS

 

Download Deep Learning Specialist Brochure PDF

Deep_Learning_specialist Ver#4

Course Curriculum

Python 3 Topics
Introduction to Python 3
Object-Oriented Programming (OOP)
Intro to data science
Machine Learning Topics
Linear algebra
Calculus
Statistics
Introduction to ML and Business cases
Data preprocessing
Regression problem
Classification problem
Clustering Problems
Reinforcement learning
Model Selection and evaluation
Result communication and report
Data Structures & Algorithms Topics
Data Structures
Basic Algorithms
Deep Learning Topics
Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Generative Adversarial Networks
Model Deployment
MISCELLANEOUS TOPICS

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