Programming13
55375AC: Fundamentals of Machine Learning
Duration
5 Days
Upcoming Batch
28 December 2026 - 01 January 2027
Batch Options
8 hours & 4 hours
Language
English / Arabic
Course Overview
The 55375AC: Fundamentals of Machine Learning course offered by CounselTrain is designed to provide a comprehensive introduction to the core concepts and techniques of machine learning. This course covers essential topics such as supervised and unsupervised learning, model evaluation, and feature selection. Participants will explore various machine learning algorithms, including linear regression, decision trees, and clustering methods, while gaining practical experience through hands-on labs and real-world case studies. The course is ideal for beginners who are new to machine learning, as well as professionals looking to strengthen their foundational understanding of the field. By the end of the course, learners will be equipped with the skills to implement basic machine learning models and understand their applications in various industries.
Course Content
8 modules · 36 topics01Module 1: Introduction to Machine Learning Models5 topics
- Understanding Machine Learning
- Understanding Machine Learning Models
- Understanding the Process for Creating a Machine Learning Model
- Reviewing Essential Math Concepts
- Using Common Python Libraries and Packages for Machine Learning
02Module 2: Understanding Classification Algorithms7 topics
- Understanding Decision Trees
- Understanding Random Forests
- Understanding Gradient Boosted Trees
- Understanding XGBoost
- Understanding Logistic Regression
- Understanding the K-Nearest Neighbors Algorithm
- What are Other Common Algorithms?
03Module 3: Creating a Classification Model3 topics
- Preparing the Data
- Building and Fitting a Model
- Testing and Validating a Classification Model
04Module 4: Understanding Binary and Non-Binary Classification3 topics
- Understanding Multi-class Classification
- Understanding the One versus Rest and One versus One Algorithms
- Understanding Multi-label Classification
05Module 5: Reviewing Statistics Concepts6 topics
- Understanding Statistical Sampling
- Understanding Measures of Central Tendency
- Calculating Measures of Dispersion
- Evaluating the Sampling Strategy
- Estimating Confidence Intervals and Sampling Error
- Quantifying the Differences between Data Distributions
06Module 6: Exploring Data and Selecting Features and Algorithms5 topics
- Graphing Data to Examine Relationships and Identify Skew
- What is Correlation and Casuality?
- Selecting Model Features
- Extracting and Scaling Features
- Creating a preprocessing pipeline
07Module 7: Measuring the Performance of a Classification Model3 topics
- Understanding Performance Measures for a Classification Model
- Understanding Regularization to Reduce Overfitting
- Evaluating a Model
08Module 8: Understanding Imbalanced Classification4 topics
- Understanding Imbalanced Classification
- Calibrating a Model
- Using Data Sampling to Balance a Dataset
- Understanding Evaulation Metrics for an Imbalanced Dataset
Schedule Dates
4 upcoming batchesSession TypePhysical sessions run at our training facility.
| Batch Dates | Duration | Batch Options | Language | Action |
|---|---|---|---|---|
| 28 December 2026 - 01 January 2027 | 5 Days | 8 hours & 4 hours | English / Arabic | |
| 29 March 2027 - 02 April 2027 | 5 Days | 8 hours & 4 hours | English / Arabic | |
| 05 July 2027 - 09 July 2027 | 5 Days | 8 hours & 4 hours | English / Arabic | |
| 11 October 2027 - 15 October 2027 | 5 Days | 8 hours & 4 hours | English / Arabic |
Can’t find a suitable date? Request a schedule that fits your team.
Request More InformationFAQs
What are the prerequisites for this course?
Participants should have basic knowledge of programming and statistics. Familiarity with Python or another programming language commonly used in data science is helpful but not required.
What skills will I gain from this course?
By completing this course, you will gain an understanding of fundamental machine learning concepts, the ability to implement basic machine learning models, and knowledge of various algorithms and techniques used in the field. You will also learn how to evaluate and optimize models for better performance.
Will there be any hands-on labs or practical exercises?
Yes, the course includes hands-on labs and practical exercises designed to help participants apply the concepts learned and gain practical experience with machine learning tools and techniques.
What support is available during the course?
CounselTrain provides instructor support, access to course materials, and additional resources such as forums or community groups to assist participants throughout the course.
How We Deliver
Flexible Training Options to Meet Your Needs
Choose how you learn — live online, in-classroom, at your workplace, or internationally. CounselTrain delivers certified IT training across the UAE in the format that fits your team.
Select the method that best suits your needs.
Online Instructor-Led Training
Learn from the comfort of your workplace or at home through live virtual sessions led by expert trainers.
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Classroom Training
Participate in interactive, face-to-face training in our top 5-star training facilities in Dubai.
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Onsite Training
Learn a customised curriculum in your workplace to ensure the most impact and team participation.
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Overseas Training
Participate in our international training sessions and improve your abilities with world-class instructors.
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