Machine Learning (Foundation) e-Learning

Machine Learning (Foundation) e-Learning

Our live, tutor-led training course on Machine Learning is designed to provide you with a solid foundation in this exciting field.

Guided by an experienced instructor in real time, this course blends interactive lectures, discussions, and practical exercises to ensure you gain both theoretical understanding and hands-on experience.

Over the course of the training, you’ll explore essential concepts such as data preprocessing, machine learning algorithms, and model evaluation techniques.

A highlight of the program is a hands-on activity where you’ll build a simple machine learning model with step-by-step guidance from your tutor.

This live format offers the unique advantage of direct interaction with the tutor, allowing you to ask questions, clarify doubts, and
receive personalized feedback throughout the session.

You’ll also participate in discussions on the ethics and future of machine learning, helping you approach this field with a well rounded perspective.

Key Benefits

  • Hands-On Learning – Build your first machine learning model using real datasets and Python.
  • Beginner-Friendly Approach – No prior experience needed — perfect for non-technical professionals or aspiring data enthusiasts.
  • Real-World Applications –Learn how ML is used across industries like marketing, finance, healthcare, and tech.
  • Job-Ready Skills – Gain practical knowledge you can apply immediately in your work or projects.
  • Expert Guidance – Live instruction from experienced ML professionals with time for Q&A.
  • Reusable Resources – Keep all training materials, code, datasets, and tools for future reference.
  • Certificate of Completion – Showcase your achievement and enhance your resume or LinkedIn profile.
  • Ethical Foundations – Understand the ethical considerations and future impact of AI and ML.

Who should complete this course

Professionals and aspiring data enthusiasts with little to no coding or ML experience ideal for analysts, marketers, product managers, developers, and anyone curious about leveraging data to solve real world problems.

Course syllabus

1. Fundamentals of Machine Learning

  • Definition and key concepts
  • Types of Machine Learning:
    – Supervised Learning
    – Unsupervised Learning
    – Reinforcement Learning
  • Real-world applications and examples

2. Data Preprocessing

  • Importance of data quality
  • Data cleaning and transformation
  • Feature selection and engineering

3. Machine Learning Algorithms

  • Overview of popular algorithms:
    – Linear Regression
    – Decision Trees
    – k-Nearest Neighbors (k-NN)
    – Support Vector Machines (SVM)
  • When to use each algorithm

4. Model Evaluation and Validation

  • Training vs. Testing datasets
  • Cross-validation techniques
  • Evaluation metrics:
    – Accuracy
    – Precision
    – Recall
    – F1-Score

5. Hands-On Activity: Building a Simple Model

  • Data loading
  • Preprocessing
  • Training
  • Evaluation
  • Building a linear regression model
  • Result analysis and improvement

6. Ethics and Future of Machine Learning

  • Ethical considerations in ML (bias, privacy, etc.)
  • Future trends and advancements

Course duration

  • This virtual class will be conducted on Zoom.
  • 4 hours.

System requirements

  • An internet connection – broadband wired or wireless (3G or 4G/LTE)
  • Speakers and a microphone – built-in, USB plug-in, or wireless Bluetooth
  • A webcam or HD webcam – built-in, USB plug-in, or:
  • An HD cam or HD camcorder with a video-capture card
  • Virtual camera software for use with broadcasting software like OBS or IP cameras

Note: On Windows devices, Zoom utilizes WebView2 and Chromium Embedded Framework
(CEF) for certain features. If not available, these are downloaded automatically by Zoom, but
admins should ensure these are whitelisted on managed devices.

Course costs

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