Machine Learning Basics
A comprehensive introduction to the foundations of machine learning, designed for beginners with basic programming knowledge.
Course Overview
About This Course
Machine Learning Basics is our foundational course designed to introduce you to the core concepts, algorithms, and practical applications of machine learning. Whether you're a professional looking to incorporate ML into your work or someone beginning their journey toward a career in data science, this course provides the essential knowledge and skills you need.
Over 8 weeks, you'll progress from understanding the fundamental principles of ML to implementing your own models using Python and popular libraries. Our hands-on approach ensures you not only grasp the theoretical concepts but can also apply them to real-world scenarios.
By the end of this course, you'll have built a solid foundation in machine learning that prepares you for more advanced studies or initial implementation in your professional work.
What You'll Learn
- Core ML concepts and terminology
- Data preprocessing techniques
- Supervised learning algorithms
- Model evaluation and validation
Career Outcomes
- Data Analyst with ML skills
- Junior ML Engineer
- Business Intelligence Specialist
- Foundation for advanced ML roles
Who Should Attend
- Analysts seeking to add ML skills
- Software developers interested in ML
- Graduates entering the data field
- Professionals starting an ML journey
Course Structure
Hands-on Sessions
Theory Lectures
Mini-Projects
Capstone Project
The course is delivered through a combination of instructor-led lectures, hands-on coding sessions, interactive workshops, and project work. Classes are scheduled twice weekly (evenings) to accommodate working professionals, with additional lab hours available for practice and project development.
Course Curriculum
Our comprehensive 8-week curriculum is designed to build your machine learning skills progressively, from fundamental concepts to practical implementation.
Module 1: Introduction to Machine Learning
Week 1- Machine learning concepts and terminology
- Types of machine learning: supervised, unsupervised, reinforcement learning
- Real-world applications and business use cases
- Python programming for machine learning: setup and essential libraries
- Hands-on: Your first ML algorithm implementation
Module 2: Data Preparation and Exploration
Week 2- Data collection and import techniques
- Exploratory data analysis with pandas and matplotlib
- Data cleaning: handling missing values and outliers
- Feature engineering and selection
- Data preprocessing and normalization techniques
Module 3: Classical Machine Learning Algorithms
Weeks 3-4- Linear and logistic regression
- Decision trees and random forests
- Support vector machines
- k-nearest neighbors algorithm
- Naive Bayes classifiers
- Hands-on implementation using scikit-learn
- Mini-project: Implementing and comparing algorithms
Module 4: Model Evaluation and Validation
Week 5- Training, validation, and test sets
- Cross-validation techniques
- Performance metrics for classification and regression
- Overfitting and underfitting: detection and prevention
- Bias-variance tradeoff
- Hyperparameter tuning and optimization
Module 5: Introduction to Unsupervised Learning
Week 6- Clustering algorithms: K-means, hierarchical clustering
- Dimensionality reduction: PCA, t-SNE
- Anomaly detection
- Association rule learning
- Mini-project: Customer segmentation using clustering
Module 6: ML in Production and Best Practices
Week 7- Model serialization and deployment basics
- Building simple ML pipelines
- Ethics and responsible ML
- Introduction to ML model monitoring
- Best practices for ML projects
- Industry case studies and lessons learned
Module 7: Capstone Project
Week 8- End-to-end ML project implementation
- Application of course concepts to real-world problem
- Project presentation and documentation
- Peer review and feedback
- Course completion ceremony and networking event
Meet Your Instructors
Our courses also feature guest lectures from industry experts and practitioners who share real-world insights and experiences.
Prerequisites & Requirements
Technical Prerequisites
- Basic Python programming knowledge (variables, functions, loops, conditionals)
- Fundamental understanding of statistics (mean, median, standard deviation)
- Comfort with basic mathematical concepts (algebra, functions, graphs)
- Familiarity with using a command line interface (basic commands)
Equipment Requirements
- Laptop with at least 8GB RAM and 20GB free storage space
- Operating system: Windows 10+, macOS 10.15+, or Linux (Ubuntu 18.04+ recommended)
- Reliable internet connection for accessing course resources and virtual sessions
- Webcam and microphone for interactive live sessions
Not Required But Helpful
- Experience with data analysis or business intelligence
- Familiarity with database concepts and SQL
- Knowledge of pandas, numpy, or other Python data libraries
Not Sure If You're Ready?
We offer a free 1-hour online assessment to help determine if this course is the right fit for your current skill level.
Schedule an assessment →Frequently Asked Questions
What if I don't meet all the prerequisites?
If you're lacking some of the technical prerequisites, we recommend completing our "Python for Data Analysis" course first or taking advantage of our pre-course preparatory materials that are provided 2 weeks before the course begins. You can also schedule a call with our admissions team to discuss your specific situation.
Is this course fully online or are there in-person sessions?
The course is offered in two formats: fully online with live sessions and a hybrid option for local students in Cyprus. The hybrid option includes in-person labs at our Nicosia training center twice a week. Both options provide the same curriculum, projects, and certification.
How much time should I expect to commit each week?
Students should expect to commit approximately 10-12 hours per week, including 4 hours of live instruction, 2-3 hours of hands-on labs, and 4-5 hours of self-study and project work. The schedule is designed to be manageable for working professionals.
What kind of support is available during the course?
Throughout the course, you'll have access to instructor support during scheduled office hours, teaching assistant support via our dedicated Slack channel, peer collaboration opportunities, and our online learning platform with additional resources and practice exercises.
Is there a job placement program after completion?
While we don't guarantee job placement, we offer career support services including resume reviews, interview preparation, and connections to our industry partners. Many graduates have successfully transitioned to roles that leverage their new ML skills, and we have an active alumni network for professional opportunities.
What certification will I receive upon completion?
Upon successful completion of the course requirements and capstone project, you'll receive the NexaLearn "Machine Learning Foundations" certification. This industry-recognized credential demonstrates your practical skills in implementing ML solutions and is valued by employers across various sectors.
Still have questions about the course?
Contact Our Admissions TeamRegister for Machine Learning Basics
Secure your spot in our upcoming cohort and begin your journey into the world of machine learning.
Course Details
Schedule & Duration
- 8 weeks, June 15 - August 10, 2025
- Live sessions: Tuesdays & Thursdays, 18:00-20:00 EEST
- Maximum 24 students per cohort
Tuition
Payment plans available: €275 × 3 monthly installments (€825 total)
10% discount for groups of 2+ registering together
What's Included
- 32 hours of live instruction
- Course materials and learning platform access
- Python development environment setup
- Project feedback and code reviews
- ML Foundations certification
- 6 months of post-course career support