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Course Description

The Machine Learning with Python program provides an in-depth introduction to machine learning techniques, tools, and applications using Python. Participants explore data preprocessing, supervised and unsupervised learning algorithms, model evaluation, and deployment strategies. The course emphasizes practical implementation, including real-world datasets, Python libraries such as scikit-learn, pandas, and NumPy, and case studies across industries. Participants gain the skills needed to build, evaluate, and optimize predictive models, understand their performance, and apply machine learning in practical scenarios.

Course Objectives

Upon completion of this course, participants will be able to:

  • Understand the fundamentals of machine learning and Python programming for ML.
  • Preprocess, clean, and visualize data for modelling.
  • Implement supervised learning algorithms (e.g., regression, classification).
  • Apply unsupervised learning techniques (e.g., clustering, dimensionality reduction).
  • Evaluate model performance using appropriate metrics.
  • Deploy machine learning models for practical applications.

Who Should Attend?

This course is designed for data analysts, software engineers, data scientists, IT professionals, and anyone interested in applying machine learning using Python in business or technical projects.

Course Agenda

Registration

Welcome & Introduction

Pre-Test

Day 1: Introduction to Machine Learning and Python

  • Overview of machine learning concepts and applications
  • Python programming basics for ML
  • Data structures, libraries (NumPy, pandas), and environment setup
  • Data exploration, visualization, and basic statistics

Day 2: Data Preprocessing and Feature Engineering

  • Handling missing values, outliers, and data scaling
  • Feature selection and extraction
  • Encoding categorical variables and normalization
  • Preparing datasets for supervised and unsupervised learning
Day 3: Supervised Learning Techniques

  • Linear and logistic regression
  • Decision trees, random forests, and ensemble methods
  • Model evaluation metrics: accuracy, precision, recall, F1-score
  • Cross-validation and hyperparameter tuning
Day 4: Unsupervised Learning and Clustering

  • Clustering techniques: K-means, hierarchical clustering
  • Dimensionality reduction: PCA, t-SNE
  • Anomaly detection using ML
  • Case studies and practical examples
Day 5: Model Deployment and Advanced Topics

  • Introduction to model deployment workflows
  • Using scikit-learn pipelines
  • Evaluating and monitoring deployed models
  • Ethics, bias, and limitations in ML
  • Program review, key takeaways, and action planning

Post Test

End of the Course

Assessment Methodology

All courses conducted by EdTech will begin with a Pre-evaluation and end with a Post-evaluation. The instructor will evaluate the knowledge and skills of the participants according to the feedback given by participants. This will help to recognize the benefits and the level of knowledge gained by participants through the course.

Training Methodology

Facilitated by a highly qualified specialist, who has extensive knowledge and experience; this program will be conducted using extensively interactive methods, encouraging participants to share their own experiences and apply the program material to real-life work situations in order to stimulate group discussions and improve the efficiency of the subject coverage.

Percentages of the total course hour classification are:

  • ​40% Theoretical lectures, Concepts and approach
  • 20% Motivation to develop individual skill and Techniques
  • 20% Case Studies and Practical Exercises
  • 20% Topic General Discussions and interaction

Course Manual

Participants will be provided with comprehensive presentation material as reference manual. This presentation material is a compilation of core valuable information, references, presentation methods and inspiring reading which will be used as a part of the material guide.

Course Certificate

At the completion of the course, all participants who successfully accomplished the required contact hours will receive an EdTech Training Participation Certificate as a testimony to their commitment to professional development and further education.

Why Edtech ?

  • Industry Experienced; Internationally Qualified Trainers
  • Hands-on Practical Sessions & Assignments
  • Intensive Study materials
  • Flexible Schedules
  • Realistic training methodology
  • High-Quality Training in Affordable Course Fees
  • Achievement Certificate, as approved by the Ministry of Education (Abu Dhabi Center for Technical and Vocational Education Training - ACTVET), HABC, AWS, IAOSHE, SHRM, etc.