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Prerequisites:

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

 

Training Program Description:

  • 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 Diploma, we will be reviewing Three main components:

 

    • First, you will be learning the basics of writing and running Python 3 scripts to more advanced features such as file operations, regular expressions, working with binary data, and using the extensive functionality of Python modules.
    • Second, you will be learning about the purpose of Machine Learning and where it applies in the real world.
    • Third, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.
    • Fourth, you will learn data structures and algorithms by solving over 80 practice problems. You will begin each Module by learning to solve defined problems related to a particular data structure & algorithm. By the end of each Module, you would be able to evaluate and assess different data structures and algorithms for any open-ended problem and implement a solution based on your design choices.
  • 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.

 

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 projects in many fields

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

 

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

 

Program Duration: 100 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)

 

DIPLOMA 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

 

Download Machine Learning Specialist Brochure PDF

Machine_Learning_specialist Ver#4

 

 

Course Curriculum

Python 3 Topics
Introduction to Python 3
Object-Oriented Programming (OOP)
Intro to data science
Data Structures & Algorithms Topics
Data Structures
Basic Algorithms
Machine Learning Topics
Linear algebra
Calculus
Statistics
Introduction to ML and Business cases
Data preprocessing
Regression problem
Classification problem
Clustering Problems
Model Selection and evaluation
Result communication and report

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