Data Science & Machine Learning (AI) Training Course

Data Science & Machine Learning (AI)

Learn to Develop cutting edge machine learning applications in various verticals – such as cyber security, image recognition, medicine, or face recognition.

Course Overivew

Data science is a field that studies data and how to extract meaning from it, whereas machine learning is a field devoted to understanding and building methods that utilize data to improve performance or inform predictions.
You will learn how to design intelligent models and advanced artificial neural networks and leverage predictive analytics to solve real-time problems in this course.
Learn about the major applications of Artificial Intelligence across various use cases across various fields like customer service, financial services, healthcare, etc.
Master the Machine Learning skills and tools used by the most innovative Artificial Intelligence teams across the globe as you delve into specializations, and gain experience solving real-world challenges.
Implement classical Artificial Intelligence and Machine Learning techniques such as search algorithms, neural networks, and tracking.
Design and build your own intelligent agents and apply them to create practical Artificial Intelligence projects including games, Machine Learning models, logic constraint satisfaction problems, knowledge- based systems, probabilistic models, agent decision-making functions and more.
Gain the ability to apply Artificial Intelligence and Machine Learning  techniques for problem- solving and explain the limitations of current Artificial Intelligence techniques.

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Course Benefits:

Data Science and Machine Learning with Python Training Course Outline

Introduction to Artificial Intelligence

  • Decoding Artificial Intelligence
  • Fundamentals of Machine Learning and Deep Learning
  • Machine Learning Workflow
  • Performance Metrics

Statistics and Linear Algebra Basics

  • Sample and population data?
  • The fundamentals of descriptive statistics
  • Measures of central tendency, asymmetry, and variability
  • Practical example: descriptive statistics
  • Distributions
  • Estimators and estimates
  • Confidence intervals: advanced topics
  • Practical example: inferential statistics
  • Hypothesis testing: Introduction
  • introduction to Probability
  • Conditional Probability.
  • Bay’s theorem.
  • Vectors vs Matrices.
  • Vectors Magnitude and direction.
  • Cos similarity between vectors.
  • Matrix operations (dot product, multiplication, addition, subtraction, inverse).
  • Covariance Matrix.
  • Eigen Vectors and Eigen Values.

Python for Data Science

  • Python Basics
    • Conditions and Loops.
    • Python Functions.
    • Python Recursion.
    • Python Classes.
  • Python Data Structures.
    • Big O notation.
    • Stacks, queues and deques.
    • Variables, Lists, Tuple, Set, dictionaries.
  • Working with Data in Python
    • Read and Write data to files.
    • Data pickling.

Data Science with Python

  • Data Science Overview
  • Data Analytics Overview
  • Statistical Analysis and Business Applications
  • Python Environment Setup and Essentials
  • Mathematical Computing with Python (NumPy)
    • Vectors and arrays.
    • Matrix Multiplication and dot product.
    • Matrix transpose and inverse matrix.
    • Statistical operations on arrays.
  • Scientific computing with Python (Scipy)
  • Data Manipulation with Pandas
    • Pandas Series and Data Frames.
    • Statistical Analysis using Pandas.
    • Data Cleaning using pandas.
    • Dummy and Categorical Data.
    • Read and write to csv and excel files.
  • Data Visualization in Python using matplotlib
    • Line, Bar, Scatter plot.
    • Histogram graph.
  • Web Scraping with Beautiful Soup.

Machine Learning

  • Introduction to Artificial Intelligence and Machine Learning
  • Data Prepossessing:
    • Feature Scaling.
    • Missing Data.
    • Dummy Variable.
    • Imbalanced Data.
  • Feature Engineering.
    • Backward Elimination.
    • Forward Elimination.
    • Model Validation.
  • Supervised Learning:
    • Linear Regression.
    • Logistic Regression.
    • Naive Bays.
    • Regression and Classification Metrics, Confusion Matrix, ROC and AUC.
  • Ensemble Learning
    • Decision Tree.
    • Random Forest.
    • Gradient Boost, XGBoost and LGBM
  • Unsupervised learning
    • K-means Clustering.
    • Hieratical Clustering.
    • Gaussian Mixture Model GMM.
    • Principle Component Analysis PCA.
  • Recommender Systems
    • Content Based.
    • Knowledge Based.
    • Collaborative Filter Based.
  • Meaning, purpose, scope, stages, applications, and effects of Artificial Intelligence
  • Fundamental concepts of Machine Learning and Deep Learning
  • Difference between supervised, semi-supervised and unsupervised learning
  • Machine Learning workflow and how to implement the steps effectively
  • The role of performance metrics and how to identify their essential methods

Statistics and Linear Algebra Basics

  • Understand the fundamentals of statistics
  • Work with different types of data
  • How to plot different types of data
  • Calculate the measures of central tendency, asymmetry, and variability
  • Calculate correlation and covariance
  • Distinguish and work with different types of distribution
  • Estimate confidence intervals
  • Perform hypothesis testing
  • Make data-driven decisions
  • Understand the mechanics of regression analysis
  • Carry out regression analysis
  • Use and understand dummy variables
  • Understand the concepts needed for data science even with Python.

Data Science with Python

  • Gain an in-depth understanding of Data Science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics
  • Install the required Python environment and other auxiliary tools and libraries
  • Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
  • Perform high-level mathematical computing using the NumPy, Pandas package and its vast library of mathematical functions.
  • Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave.
  • Perform data analysis and manipulation using data structures and tools provided in the Pandas package
  • Gain expertise in Machine Learning using the Scikit-Learn package
  • Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline
  • Use the Scikit-Learn package for natural language processing
  • Use the matplotlib library of Python for data visualization
  • Extract useful data from websites by performing web scraping using Python Integrate Python with Hadoop, Spark, and MapReduce

Machine Learning

  • Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modeling
  • Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach that includes working on four major end-to-end projects and 25+ hands-on exercises
  • Acquire thorough knowledge of the statistical and heuristic aspects of Machine Learning
  • Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python
  • Validate Machine Learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithm, which include Boosting & Bagging techniques
  • Comprehend the theoretical concepts and how they relate to the practical aspects of Machine Learning

Artificial Intelligence course is well suited for a variety of profiles like:

  • Developers aspiring to be an ‘Artificial Intelligence Engineer’ or Machine Learning engineers
  •  Analytics managers who are leading a team of analysts
  •  Information architects who want to gain expertise in Artificial Intelligence algorithms
  •  Graduates looking to build a career in Artificial Intelligence and Machine Learning


This course is available at :

Classroom Training


Online Training

Virtual Interactive Instructor LED
Self-Paced Training



We have been in the market since 1995, and we kept accumulating experience in the training business, and providing training for more than 100,000 trainees ever since, in Egypt, and the MENA region.

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Up To Date

We keep tabs on every change in the market and the technology field, so our training programs will always be updated up to the World-class latest standards, and adapted to the global shape-shifting job market.

Certified Instructors

We select the best instructors, who are certified from trustworthy international vendors. They share their professional experience with the Trainees, so they can have a clear hands-on experience.

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