learn machine learning techniques such as data transformation and algorithms that can find patterns in data and apply
machine learning algorithms to tasks of their own design.

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.

This program is comprised of 3 courses and 3 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:


  • Introduction To AI Program
    • Intermediate Python programming knowledge, including:
      ○ At least 40hrs of programming experience
      ○ Familiarity with data structures like dictionaries and lists
      ○ Experience with libraries like NumPy and pandas
    • Basic knowledge of probability and statistics, including:
      ○ Experience calculating the probability of an event
      ○ Familiarity with terms like the mean and variance of a probability distribution


Training Topics:

  • Intro to machine learning
  • Data preprocessing
  • Supervised Learning
    • Regression
    • Perceptron Algorithms
    • Decision Trees
    • Naive Bayes’
    • Support Vector Machines
    • Ensemble of Learners
    • Evaluation Metrics
    • Training and Tuning Models
  • Neural Networks
    • Introduction to Neural Networks
    • Implementing Gradient Descent
    • Training Neural Networks
    • Deep Learning with PyTorch
  • Unsupervised Learning
    • Clustering
    • Hierarchical and Density-Based Clustering
    • Gaussian Mixture Models
    • Dimensional Reduction
  • Reinforcement learning
  • Model selection and evaluation


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.


Program Duration: 40 hours

Program Language: English / Arabic



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 Curriculum

Intro to machine learning
Data preprocessing
Supervised Learning
Neural Networks
Unsupervised Learning
Reinforcement learning
Model selection and evaluation

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