Certificate Program in Machine Learning

  • Onsite and Online
  • Intakes: Jan, Apr, Jul, Oct

This course includes 12 modules focused on introducing machine learning concepts, data processing and model evaluation. It begins with basic programming and data handling, followed by an exploration of machine learning algorithms and tools. Hands-on exercises reinforce theoretical knowledge, guiding students to create simple models, interpret results and recognize the potential and limitations of machine learning.

Course Description

This Certificate Program in Machine Learning is designed to introduce individuals with minimal technical experience to the exciting field of machine learning (ML). Covering fundamental concepts and practical skills, this program enables students to understand and apply ML basics. Each module is carefully designed to progressively build knowledge and introduce tools used by data scientists and machine learning practitioners.

By the end of this program, students will be able to:

  • Understand key machine learning concepts and algorithms.
  • Perform basic data handling and preprocessing tasks.
  • Apply beginner-level machine learning models on datasets.
  • Evaluate and interpret machine learning results.
  • Recognize the ethical implications of machine learning in real-world applications.

This program is ideal for beginners, including high school graduates, career changers and entry-level professionals in non-technical fields who have basic computer skills and want to explore machine learning.

Module 1: Introduction to Machine Learning 

Overview of ML applications and impact.

Differences between supervised and unsupervised learning.

Understanding data, features, and labels.

 
Module 2: Basics of Python Programming 

Introduction to Python for ML.

Variables, data types, loops, and basic functions.

Simple data manipulations using Python.

 
Module 3: Data Handling and Libraries in Python 

Introduction to ML libraries: NumPy, pandas, and Matplotlib.

Loading, cleaning and exploring data.

Basic data visualization techniques.

 
Module 4: Introduction to Statistics for Machine Learning 

Descriptive statistics, mean, median and mode.

Variance, standard deviation and distributions.

Probability basics and importance in ML.

 
Module 5: Exploratory Data Analysis (EDA) 

Identifying data trends and outliers.

Analyzing patterns through visualizations.

Data transformation and feature scaling.

 
Module 6: Supervised Learning: Linear Regression 

Fundamentals of linear regression.

Building and evaluating a linear regression model.

Practical exercises using simple datasets.

 
Module 7: Supervised Learning: Classification Algorithms 

Introduction to classification tasks and algorithms.

Understanding K-Nearest Neighbors (KNN) and decision trees.

Creating and evaluating a classification model.

 
Module 8: Unsupervised Learning: Clustering 

Introduction to clustering and K-means algorithm.

Use cases for clustering in real-world scenarios.

Practical exercises on clustering with small datasets.

 
Module 9: Model Evaluation and Metrics 

Importance of model evaluation in ML.

Metrics for classification (accuracy, precision, recall).

Understanding confusion matrix and cross-validation.

 
Module 10: Introduction to Neural Networks 

Basics of artificial neural networks and deep learning.

Simple neural network structure and working principles.

Overview of common applications of neural networks.

 
Module 11: Practical Project: Building a Simple ML Model 

Applying knowledge from previous modules.

Choosing a dataset, defining the ML problem and implementing a model.

Model evaluation and interpreting results.

 
Module 12: Ethics and Future of Machine Learning 

Understanding ethical concerns in ML (bias, fairness, privacy).

Real-world examples and impacts of ML applications.

Future trends in machine learning and AI.

 
The delivery method for this certification is entirely online, requiring candidates to have access to a personal computer.

Sinhala and Simple English

30 Hours
2 Hours, 3 Days per week
Rs 15,000/= 

How to Apply

  • You Apply

Tell us a little about yourself and we’ll help with the rest. Our convenient online application tool only takes 10 minutes to complete.

  • We Connect

After you submit your application, an admissions representative will contact you and will help you to complete the process.

  • You Get Ready

Once you’ve completed your application and connected with an admissions representative, you’re ready to create your schedule.

How To Apply

Your Application

Tell us a little about yourself and we’ll help with the rest. Our convenient online application tool only takes 10 minutes to complete.

Our Response

After you submit your application, an admissions representative will contact you and will help you to complete the process.

Your Journey

Once you’ve completed your application and connected with an admissions representative, you’re ready to create your schedule.

FORM

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