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Prerequisites:

  • Basic skills with at least one programming language are desirable – optional
  • Familiar with the basic math and statistic concepts – optional

 

Training Program Description:

  • Build expertise in data manipulation, visualization, predictive analytics, machine learning, and data science. With the skills you learn in a program, you can launch or advance a successful data career. Start acquiring valuable skills right away, create a project portfolio to demonstrate your abilities, and get support from mentors, peers, and experts in the field.
  • The demand for Machine Learning and Data Science 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 and Data science skills by employers — and the job salaries of Machine Learning and Data Science practitioners — are only bound to increase over time, as AI becomes more pervasive in society. Machine Learning and Data Science are a future-proof career.
  • Gain real-world data science experience with projects designed by industry experts. Build your portfolio and advance your data science and machine learning career.
  • Throughout this program, you will practice your Data Science and 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 will also complete the program by preparing a Data Science and Machine Learning capstone project that will showcase your applied skills to prospective 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 EAII 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:  thanos.py
  • Project 2:  Design a Bank system
  • Project 3:  Play with SF Salaries dataset from Kaggle
  • Project 4:  Titanic Analysis Project
  • Project 5:  911 calls dataset from Kaggle analysis
  • Project 6:  Stock Market Analysis Project
  • Project 7:  Predict student marks based on hours of study
  • Project 8:  Predict Startup profit
  • Project 9:  Predict Salary based on Years of experience
  • Project 10: Predict Loan Approval Problem
  • Project 11: Advertising Problem
  • Project 12: Sentiment Analysis Problem
  • Project 13: Adult Income Problem
  • Project 14: IRIS Problem
  • Project 15: Mall Problem
  • Project 16: University Problem
  • Project 17: Weather Logging system
  • Project 18: Design Database systems like Facebook, Souq, YouTube
  • Project 19: Scrap books and design a database
  • Project 20: Salary predictor web application deployment on Heroku
  • Capstone Project

 

program outcomes:

  • 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.
  • analyze, design, and document a system component using appropriate data analysis techniques and models.
  • demonstrate an understanding of the fundamental principles of data analytics systems and technologies.
  • Able to use standard techniques of mathematics, probability, and statistics in order to address problems typical of a career in data science
  • Apply appropriate modeling techniques to conduct quantitative analyses of complex big data sets
  • Use statistical software packages such as Python to solve data science problems
  • Communicate results effectively to stakeholders.
  • Use principles of statistics and probability to design and execute A/B tests and recommendation
  • The transition from the Very Basics to a Point Where You Can Effortlessly Work with Large SQL Queries
  • Web Scraping using Python
  • scrape data and store it locally or globally to access the data sets whenever needed
  • Deploy machine learning models into the cloud
  • Send and receive requests from deployed machine learning models
  • Build reproducible machine learning pipelines
  • Create continuous and automated integrations to deploy your models
  • Build a machine learning model APIs
  • Design testable, version-controlled, and reproducible production code for model deployment

 

Program Duration: 15 Weeks

Program Language: English / Arabic

Location: EPSILON AI INSTITUTE | Head Office

Participants will be granted a completion certificate from Epsilon AI 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)

 

CURRICULUM

1.Python 3

  • Git & GitHub
    • What is the use of version control?
    • Install Git
    • Create repo
    • Check Status
    • Add changes to staging area
    • Commit changes
    • Show commits log
    • .gitignore
    • Branches
    • What is GitHub
    • Clone repo
    • Push & Pull
    • Use Git Kraken GUI
    • Fork projects
    • Pull requests
    • Write good documentation
  • Environment Setup (Anaconda)
  • Command Line
  • Conda & pip package managers
  • Jupyter Notebook
  • Input & Output
  • Variables
  • Data types
    • Numbers & Math
    • Boolean & Comparison and Logic
    • Strings
    • Lists
    • Tuples
    • Sets
    • Dictionaries
  • File Handling
  • If Conditions
  • For Loops
  • Built-in functions & Operators (zip, enumerate, range, …)
  • List Comprehensions
  • Functions
  • Lambda Expressions
  • Map, Filter, Reduce
  • Variables Scope
  • Modules & Packages
  • Project #1 (Thanos.py)
  • Object-Oriented Programming (OOP)
    • Classes & Objects
    • Data Hiding and Encapsulation
    • Inheritance
    • Exceptions
    • Project #2 (Design a Bank system)

 

2.Mathematics For AI

  • Calculus
    • Rate of Change
    • First order and second order derivatives
    • Partial Derivatives
    • Gradient Descent
    • Chain Rule
  • Linear Algebra
    • Vectors operations
    • Matrix operations
    • Vector Transformation using Matrices
  • Probability
    • Probability Basics
    • Conditional Probability
    • Random Variables and Random processes
  • Statistics
    • Central Tendency
    • Measures of Dispersions
    • Data Visualization
    • Probability Density Function and Distributions
    • Normal Distributions
    • Standard Normal Distributions
    • Correlation and co-variance
    • Sample Distribution
    • Central Limit Theorem
    • Confidence Interval
    • Statistical Significance
    • Hypothesis Testing

 

3.Exploratory Data Analysis with NumPy & Pandas

  • NumPy
    • Install and use NumPy
    • Create NumPy Array
    • Built in Methods
    • Reshaping
    • Indexing
    • Selection
    • Arithmetic and Logic
    • Universal Array Functions
  • Pandas
    • Series
    • Data Frames
    • Deal with Missing data
    • Grouping data and aggregate functions
    • Merging, Joining and Concatenating
    • Pivoting
    • Useful Methods
    • Apply function
    • Data Input & Output
    • Project #3 (Play with SF Salaries dataset from Kaggle)

 

4.Data Visualization with Matplotlib & Seaborn

  • Data Visualization
    • Matplotlib and Seaborn
    • Distribution Plots
    • Categorical Plots
    • Matrix Plots
    • Regression Plots
    • Color palettes
    • Change Plot Size
  • Project#4 (Titanic Analysis Project)
  • Project #5 (911 calls dataset from Kaggle analysis)

 

5.Data Preprocessing & ETL

  • Data Preprocessing
  • Feature Engineering and Extraction
    • Domain knowledge features
    • Date and Time features
    • String operations
    • Web Data
    • Geospatial features
    • Work with Text
  • Feature Transformations
    • Data Cleaning or Cleansing
    • Work with Missing data
    • Work with Categorical data
    • Split data to Train and Test Sets
    • Feature Scaling
    • Detect and Handle Outliers
    • Deal with Imbalanced classes
    • How to use Pipelines
  • Project #6 (Stock Market Analysis Project)
  • Feature Selection
    • Filter Methods
    • Wrapper Methods
    • Embedded Methods

 

6.Machine Learning

  • Supervised Learning
    • Regression
      • Simple Linear Regression
      • Project #7 (Predict student marks based on hours of study)
      • Multiple Linear Regression
      • Project #8 (Predict Startup profit)
      • Polynomial Regression
      • Project #9 (Predict Salary based on Years of experience)
    • Classification
      • Logistic Regression
      • Project #10 (Predict Loan Approval Problem)
      • K-Nearest Neighbors (KNN)
      • Project #11 (Advertising Problem)
      • Naive Bayes
      • Project#12 (Sentiment Analysis Problem)
      • Decision Trees
      • Project #13 (Adult Income Problem)
      • SVM
      • Project#14 (IRIS Problem)
      • Ensemble Methods
      • Bagging & Boosting
      • Random Forests
      • XGBoost
      • Project#13 (Adult Income Problem) – cont.
      • Evaluating Model Performance
    • Unsupervised Learning
      • Clustering
        • K-Means
        • Project #15 (Mall Problem)
        • Project #16 (University Problem)
        • Hierarchical Clustering
        • Density-Based Clustering – DBSCAN
        • Project#15 (Mall Problem) – cont.
        • Project#16 (University Problem) -cont.
      • Dimension Reduction
        • PCA
      • Evaluating Model Performance
    • Model Selection & Evaluation
      • Cross-Validation
      • Hyperparameter Tuning
        • Grid Search
        • Randomized Search

 

 

7.Web Scraping & Web Services

  • Network Topologies
  • Internet and Web Servers
  • HTTP Request/Response Cycle
  • HTML
  • CSS
  • Scrapping Concept
  • Beautiful Soap Library
  • Project #17 (Weather Logging system)

 

8.Databases & SQL

  • Tables, Columns, and Data types
  • How to design a database.
  • One-To-Many & Many-To-Many Relationships.
  • Project #18 (Design Database systems like Facebook, Souq, YouTube)
  • SQL
  • CRUD
  • Selecting data
  • Filtering data
  • Ordering data
  • Limiting data
  • Aggregate Functions
  • Joining tables
  • Grouping data
  • Subqueries
  • Inserting new data
  • Updating data
  • Deleting data
  • Python and SQLite
  • DB Browser for SQLite
  • Project #19 (Scrap books and design a database)

 

9.Software Engineering & Model Deployment

  • What is Internet and Web Servers
  • HTTP Request/Response Cycle
  • HTML
  • CSS
  • Python as a backend language
  • Flask
  • Work with requests
  • Work with templates
  • Integrate machine learning model
  • Git & GitHub Heroku
  • Project #20 (Salary predictor web application deployment on Heroku)

 

10.Advance your Career

  • Boost your Profile on Kaggle
  • Build up your online presence
    • Medium Blog
    • YouTube Channel
    • Contribute to Open Source Community on GitHub
  • Build your Resume
  • LinkedIn and Networking
  • Learn how to seek a job

 

11.AI in Business

  • AI product development cycle
  • Measuring Business Impact & Mitigating Bias
  • Collect requirements and do market research
  • Build an AI Team
  • Agile Methodology
  • Developing a business proposal for an AI product
  • How to scale up your idea

 

12.CAPSTONE PROJECT

 

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    Course Curriculum

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