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Prerequisites:

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

 

Training Program Description:

  • This program will teach you all the tools needed to succeed in your journey into the world of AI.
  • Make sure to set aside adequate time on your calendar for focused work. In order to succeed, this program will teach you how to become a better Artificial Intelligence or Machine Learning Engineer by teaching you classical AI algorithms applied to common problem types. You will complete projects and exercises incorporating search, optimization, planning, and probabilistic graphical models which have been used in Artificial Intelligence applications for automation, logistics, operations research, and more. These concepts form the foundation for many of the most exciting advances in AI in recent years. Each project you build will be an opportunity to demonstrate what you’ve learned in your lessons and become part of a career portfolio that will demonstrate your mastery of these skills to potential employers.

 

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:

o Project 1: Exploring the Titanic Survival Data
o Project 2: Predicting Housing Prices
o Project 3: Finding Donors for Charity
o Project 4: Creating Customer Segments Deep learning
o project 5: Dog Breed Recognition
o Project 6: Teach a Quad copter to Fly
o Project 7: Your First Neural Network
o Project 8: Generate Faces
o Project 9: Build a Sudoku Solver
o Project 10: Build a Forward Planning Agent
o Project 11: Build an Adversarial Game Playing Agent
o Project 12: Part of Speech Tagging

Capstone projects in many fields:

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

 

Program Duration: 200 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
  • Object-Oriented Programming (OOP)
    • Special Functions
    • Strings
    • Classes
    • Inheritance
    • Regular expressions
    • Working with files
    • Python generators
    • Python Decorators
    • Exceptions
    • Regular expressions
    • Multithreading and multiprocessing Sockets and APIs
  • Introduction to Gui
    • Gui grid
    • Gui events
    • Gui styles
  • Intro to data science
    • Database with SQLite
    • Numpy and matrix operations
    • Pandas
    • Matplotlib
    • Building your own server
    • Data visualization
    • Git command line and GUI based
    • Web Scraping for data collecting

 

II- Machine Learning Topics

  • Linear algebra
  • Calculus
  • Statistics
  • 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
  • Data preprocessing
    • Importing libraries
    • Data acquisition
    • Data cleaning
    • Handling missing data
    • Categorical data
    • Data splitting
    • Feature scaling
  • 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
  • Clustering Problems
    • Dimensionality reduction
    • K-means
    • DBSCAN
    • hierarchical clustering
    • Association Rules
  • Reinforcement learning
    • Upper confidence bond
    • Thompson sampling
  • Model Selection and evaluation
    • Loss functions
    • Gradient descent
    • Bias-variance tradeoff
    •  Cross-validation
    • Hyperparameter tuning
  • Result communication and report

 

III- 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
  • Basic 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
  • Advanced Algorithms
    • Greedy Algorithms
    • Graph Algorithms
    • Dynamic Programming
    • Linear Programming
    • Practice Problems: Graph Traversals, Diijkstra’s Algorithm, Shortest Hops, A* Search, Longest Palindromic subsequence, web crawler

IV- Deep Learning Topics

  • Neural Networks
    • INTRODUCTION TO NEURAL NETWORKS
    • IMPLEMENTING GRADIENT DESCENT
    • TRAINING NEURAL NETWORKS
    • SENTIMENT ANALYSIS
    • DEEP LEARNING WITH PYTORCH
  • Convolutional Neural Networks
    • INVARIANCE, STABILITY
    • CLOUD COMPUTING
    • CONVOLUTIONAL NEURAL NETWORK
    • CNNS IN PYTORCH
    • PROPERTIES OF CNN REPRESENTATIONS: INVERTIBILITY, STABILITY, INVARIANCE.
    • WEIGHT INITIALIZATION
    • AUTOENCODERS
    • VARIATIONAL AUTOENCODERS
    • TRANSFER LEARNING IN PYTORCH
    • DEEP LEARNING FOR CANCER DETECTION
    • 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
    • SENTIMENT PREDICTION RNN
  • 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

 

 

V-Artificial Intelligence Professional Topics

  • Intro to Artificial Intelligence
    • Intro to Artificial Intelligence
    • Setting Up your Environment with Anaconda
  • Constraint Satisfaction Problems
    • Solving Sudoku With AI
    • Constraint Satisfaction Problems
    • Advanced Topics in CSP
  • Classical Search
    • Introduction
    • Uninformed Search
    • Informed Search
    • Advanced Topics: Search
    • Exercise: Search ➔ Implement informed & uninformed search for Pacman
  • Optimization Problems
    • Introduction
    • Hill Climbing
    • Simulated Annealing
    • Genetic Algorithms
    • Additional Optimization
    • Exercise: Optimization Problems Topics
  • Automated Planning
    • Symbolic Logic & Reasoning
    • Introduction to Automated Planning
    • Classical Planning
    • Advanced Topics in Planning

 

  • Adversarial Search
    • Search in Multi-Agent Domains
    • Optimizing Minimax Search
    • Extending Minimax Search
    • Advanced Adversarial Search Topics
  • Probabilistic Models & Pattern Recognition
    • Search in Multi-Agent Domains
    • Optimizing Minimax Search
    • Extending Minimax Search
    • Advanced Adversarial Search Topics

 

Download Artificial Intelligence Professional Brochure PDF

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Course Curriculum

Python 3 Topics
Introduction to Python 3
Object-Oriented Programming (OOP)
Introduction to Gui
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
Advanced Algorithms
Deep Learning Topics
Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Generative Adversarial Networks
Model Deployment
MISCELLANEOUS TOPICS
Artificial Intelligence Professional Topics
Intro to Artificial Intelligence
Constraint Satisfaction Problems
Classical Search
Optimization Problems
Automated Planning
Adversarial Search
Probabilistic Models & Pattern Recognition

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