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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 future-proof careers.
  • 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:  py
  • Project 2:  Library System using OOP.
  • Project 3:  Weather Logs data collecting system.
  • Project 4: Employee data collecting using web services.
  • Project 5:  Design Database systems like Facebook, Souq, YouTube
  • Project 6:  Weather Logs data collecting system using database
  • Project 7:  Analyze SF Salaries dataset from Kaggle.
  • Project 8:  Analyze the eCommerce Purchase dataset from Kaggle.
  • Project 9:  Titanic Analysis Project
  • Project 10: 911 calls dataset from Kaggle analysis
  • Project 11: Preprocess Loan data
  • Project 12: Ecommerce Expenses Prediction
  • Project 13: Kaggle Bike Demand Predictions
  • Project 14: Kaggle Black Friday Purchase Predictions
  • Project 15: Predict Loan Approval Problem
  • Project 16: Advertising Problem
  • Project 17: Sentiment Analysis Problem
  • Project 18: Mall Problem
  • Project 19: University Problem
  • Project 20: Bike demand 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 fundamental principles of data analytics systems and technologies.
  • Able to use standard techniques of mathematics, probability, and statistics 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 recommendations.
  • 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 machine learning model APIs.
  • Design testable, version-controlled, and reproducible production code for model deployment.
  • Perform feature engineering to improve the performance of machine learning models.
  • 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.
  • Boost your Profile.
  • how to solve the human capital, technological, and management challenges of building data science into the business
  • identifying opportunities for data science across many functional areas of the business
  • learn the tools to prioritize and execute on those opportunities as part of a data science initiative.

 

Program Duration: 18 Weeks

Program Language: English / Arabic

Location: EPSILON AI INSTITUTE | Head Office / Virtual Online Live Classroom

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

  • Introduction to Computer science
  • 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
  • Modules & Packages
  • Git & GitHub
  • Project #1 (Thanos.py)
  • Object-Oriented Programming (OOP)
    • Classes & Objects
    • Data Hiding and Encapsulation
    • Inheritance
    • Exceptions
    • Project #2 (Library System using OOP)

 

2.Mathematics For AI

  • Calculus
    • Rate of Change
    • First-order and second-order derivatives
    • Partial Derivatives
    • Gradient Descent
    • Chain Rule
    • Integration
  • Linear Algebra
    • Vector's operations
    • Matrix operations
  • Probability
    • Probability Basics
    • Combinatorics
    • Bayes Rules
    • Conditional Probability
  • Statistics
    • Central Tendency
    • Measures of Dispersions
    • Data Visualization
    • Probability Density Function and Distributions
    • Normal Distributions
    • Standard Normal Distributions
    • Correlation and covariance
    • Sample Distribution
    • Central Limit Theorem
    • Confidence Interval
    • Statistical Significance
    • Hypothesis Testing

 

3. Web Scraping & Web Services

  • Network Topologies
  • Internet and Web Servers
  • HTTP Request/Response Cycle
  • HTML
  • CSS
  • Scrapping Concept
  • Beautiful Soap Library
  • Web Services & JSON
  • Project #3 (Weather Logs data collecting system)
  • Project #4 (Employee's data collecting using web services)

 

4. Databases & SQL

  • RDBMS
  • Tables, Columns, and Data types
  • How to design a database.
  • One-To-Many & Many-To-Many Relationships.
  • Project #5 (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 #6 (Weather Logs data collecting system using database)

 

5. Exploratory Data Analysis with NumPy & Pandas

  • NumPy
  • Pandas
  • Project #7 (Analyze SF Salaries dataset from Kaggle)
  • Project #8 (Analyze Ecommerce Purchase dataset from Kaggle)

 

6. Data Visualization with Matplotlib & Seaborn

  • Data Visualization
  • Project #9 (Titanic Analysis Project)
  • Project #10 (911 calls dataset from Kaggle analysis)

 

7. Data Preprocessing & ETL

  • 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
    • Detect and Handle Outliers
    • Deal with Imbalanced classes
    • Split data to Train and Test Sets
    • Feature Scaling
    • Project #11 (Preprocess Loan data)

 

8. Data Analysis Final Project

  • Data Analysis Final Project Discussion

 

9. Machine Learning

  • Supervised Learning
    • Regression
      • Simple Linear Regression
      • Multiple Linear Regression
      • Other Regression Methods
      • Evaluating Model Performance
      • Project #12 (Ecommerce Expenses Prediction)
      • Project #13 (Kaggle Bike Demand Predictions)
      • Project #14 (Kaggle Black Friday Purchase Predictions)
    • Classification
      • Logistic Regression
      • K-Nearest Neighbors (KNN)
      • Naive Bayes
      • Decision Trees
      • SVM
      • Ensemble Methods
      • Bagging & Boosting
      • Random Forests
      • XGBoost
      • Evaluating Model Performance
      • Project #15 (Predict Loan Approval Problem)
      • Project #16 (Advertising Problem)
      • Project #17 (Sentiment Analysis Problem)
    • Intro Deep Learning
  • Unsupervised Learning
    • Clustering
      • K-Means
      • Hierarchical Clustering
      • Project #18 (Mall Problem)
      • Project #19 (University Problem)
    • Dimension Reduction
      • PCA
    • Model Selection & Evaluation
      • Cross-Validation
      • Hyperparameter Tuning
        • Grid Search
        • Randomized Search

10. 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 the machine learning model.
  • Deploy the app to Heroku
  • Project #20 (Bike demand predictor web application deployment on Heroku)
  • Capstone Project

 

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