Machine Learning

Professional Training Syllabus

  • Total Duration: 120 Hours
  • Training Type: Theory + Hands-on Practice + Projects
  • Level: Beginner to Intermediate
  • Mode: Classroom / Online (Instructor-Led)
  • Tools Covered: Python, Machine Learning Libraries, Data Processing Tools, Visualization Tools

Programme Overview

The Machine Learning training program is designed to help learners understand how systems learn from data and make predictions without being explicitly programmed. This course focuses on building a strong foundation in machine learning concepts, data handling, and model-building techniques used in real-world applications.

Course Objective

The objective of this Machine Learning course is to help learners understand how data-driven models are built, trained, and evaluated. The program aims to develop practical skills in working with data, applying machine learning algorithms, and interpreting model results.

By the end of the course, learners will be able to build basic machine learning models, analyze data patterns, and apply machine learning techniques to solve real-world problems, preparing them for entry-level machine learning and analytics roles.

Course Overview

Machine Learning is a key technology that allows systems to learn from data and improve performance over time without being explicitly programmed. This course provides a structured introduction to machine learning concepts, data handling, and model-building techniques used in real-world applications.

The training focuses on practical understanding through real datasets, hands-on exercises, and projects. Learners gain exposure to how machine learning is applied in areas such as business analytics, finance, healthcare, marketing, and automation. The course is designed to be beginner-friendly while building strong foundations for advanced learning.

What You Learn

  • Understand the fundamentals of Machine Learning and how it works

  • Learn how to prepare, clean, and organize data for machine learning models

  • Use Python to work with datasets and build basic ML solutions

  • Apply supervised and unsupervised learning techniques

  • Build and train machine learning models for prediction and classification

  • Evaluate model performance and improve accuracy

  • Understand real-world applications of machine learning in business and technology

  • Work on hands-on projects using real datasets

  • Interpret results and explain model outcomes clearly

Learning Outcomes

After completing the Machine Learning course, learners will be able to:

  • Understand core machine learning concepts and workflows

  • Prepare and process data for machine learning models

  • Build basic supervised and unsupervised learning models

  • Train, test, and evaluate machine learning models

  • Identify suitable machine learning techniques for real problems

  • Interpret model results and explain outcomes clearly

  • Apply machine learning concepts in business and technology scenarios

    MAHIRA EDGE Advantage

    Mahira Edge offers industry-focused Machine Learning training that combines clear concept explanation with hands-on practical learning. The course is designed to help learners build real skills that are relevant to current industry requirements.

    Learners benefit from experienced trainers, practical projects, real-world datasets, and continuous learning support. Mahira Edge focuses on building confidence, problem-solving ability, and job readiness through guided sessions, project mentoring, and interview preparation.

    Who Can Join This Course?

    • Students from any educational background

    • Fresh graduates looking to start a career in technology

    • Working professionals who want to upgrade their skills

    • IT professionals aiming to move into Machine Learning or AI

    • Data Analysts who want to advance into Machine Learning roles

    • Career switchers interested in data-driven technologies

    • Beginners with basic computer knowledge (no prior ML experience required)

      Career Opportunities After Machine Learning Course

      After completing this course, learners can explore roles such as:

      • Machine Learning Analyst

      • Junior Machine Learning Engineer

      • Data Analyst (with Machine Learning skills)

      • AI & Automation Associate

      • Business Analytics Professional

      • Predictive Analytics Specialist

      • Technology & Data Solutions Analyst

      Detailed Syllabus Machine Learning

      Module 1: Introduction to Machine Learning
      • What is Machine Learning and how it works
      • Difference between AI, Machine Learning, and Data Science
      • Types of Machine Learning
      • Real-world applications of Machine Learning
      Module 2: Data Fundamentals for Machine Learning
      • Types of data and data sources
      • Data collection methods
      • Data cleaning and preprocessing
      • Handling missing and inconsistent data
      Module 3: Python for Machine Learning
      • Python basics for ML
      • Working with data using Python
      • Introduction to NumPy and Pandas
      • Data preparation for ML models
      Module 4: Exploratory Data Analysis (EDA)
      • Understanding data patterns
      • Data visualization basics
      • Feature understanding
      • Identifying trends and outliers
      Module 5: Supervised Learning Algorithms
      • Linear regression
      • Logistic regression
      • Decision trees
      • K-Nearest Neighbors (KNN)
      • Model training basics
      Module 6: Unsupervised Learning Algorithms
      • Clustering concepts
      • K-Means clustering
      • Hierarchical clustering
      • Association rule basics
      Module 7: Model Evaluation & Improvement
      • Training and testing data
      • Performance metrics
      • Overfitting and underfitting
      • Model tuning basics
      Module 8: Machine Learning Applications
      • Predictive analysis
      • Recommendation system overview
      • Business and industry use cases
      • Automation basics
      Module 9: Projects & Case Studies
      • Hands-on ML mini projects
      • Real-world datasets
      • End-to-end project workflow
      • Project presentation
      Module 10: Career & Interview Preparation
      • Machine Learning interview basics
      • Resume and project explanation
      • Career roadmap in ML
      • Job role understanding