5-Day Machine Learning Training Course | ML Basics, MLOps & Use Cases

📘 What is Machine Learning (ML)?

Machine Learning (ML) is a subset of Artificial Intelligence (AI) that allows computers to learn from data and improve performance automatically without being explicitly programmed.

Instead of writing rules for every situation, we feed data to algorithms that find patterns, make predictions, or take decisions.

👉 Example:

• Traditional programming: Write code that defines rules → “If fever > 101°F and cough → suspect flu.”
• ML: Feed thousands of patient records → model learns rules itself → predicts flu probability.

🔑 Types of Machine Learning

There are four main categories:

1. Supervised Learning

• Definition: The model is trained on labeled data (input + correct output).
• Goal: Predict outcomes for new data.
• Techniques: Regression, Classification.
• Examples:
  • Predicting house prices (Regression)
  • Spam email detection (Classification)

2. Unsupervised Learning

• Definition: The model works on unlabeled data to find hidden patterns.
• Goal: Discover structure in data.
• Techniques: Clustering, Dimensionality Reduction.
• Examples:
  • Customer segmentation in marketing
  • Grouping news articles by topic

3. Reinforcement Learning (RL)

• Definition: An agent learns by interacting with an environment, receiving rewards or penalties.
• Goal: Maximize cumulative reward.
• Examples:
  • Self-driving cars learning to drive safely
  • AI playing chess or Go
  • Robot learning to walk

4. Semi-Supervised & Self-Supervised Learning (hybrids)

• Semi-Supervised: Mix of labeled + unlabeled data (useful when labeling is expensive).
• Self-Supervised: Models generate their own labels from raw data (common in NLP and Generative AI).
• Examples:
  • Medical imaging (few labeled scans + many unlabeled)
  • Large Language Models (LLMs) like GPT trained on self-supervised objectives

🏥 Use Cases of Machine Learning (Across Sectors)

Healthcare

• Disease prediction (diabetes, cancer detection from scans)
• AI-assisted radiology & pathology
• Drug discovery & personalized treatment

Finance & Banking

• Fraud detection in credit card transactions
• Credit scoring & loan risk assessment
• Algorithmic trading

Retail & E-commerce

• Product recommendation engines (Amazon, Netflix)
• Dynamic pricing optimization
• Demand forecasting

Telecom

• Customer churn prediction
• Network fault detection & predictive maintenance
• Optimizing bandwidth usage

Manufacturing & IoT

• Predictive maintenance of machines
• Quality control with computer vision
• Supply chain optimization

Transportation & Smart Cities

• Self-driving vehicles (Tesla, Waymo)
• Traffic flow optimization
• Smart energy grid management

✅ Summary

• Machine Learning = systems that learn from data to make predictions/decisions.
• Types = Supervised, Unsupervised, Reinforcement, Semi/Self-Supervised.
• Use cases = Found in every industry: healthcare, finance, telecom, retail, manufacturing, transport, etc.

 

📘 5-Day Basic Training Course on Machine Learning

🎯 Course Objective

To provide participants with a practical introduction to Machine Learning (ML), its techniques, tools, and real-world applications, while also covering project workflows and MLOps essentials.

🗓 Day-by-Day Breakdown

Day 1: Introduction to Machine Learning

• Topics:
  • What is ML? Difference between AI, ML, and DL
  • Types of ML: Supervised, Unsupervised, Reinforcement
  • Common ML Algorithms (Linear Regression, Decision Trees, Clustering, etc.)
  • ML lifecycle: Data → Training → Deployment
  • Hands-on demo: Training a simple ML model (classification or regression)
• Outcome: Participants understand ML basics and can build their first ML model.

Day 2: Data & Feature Engineering

• Topics:
  • Importance of data in ML
  • Data preprocessing (cleaning, normalization, missing values)
  • Feature engineering & feature selection
  • Training vs. testing datasets
  • Overfitting & underfitting concepts
  • Hands-on: Preparing a dataset and evaluating model accuracy
• Outcome: Participants learn how to prepare data for ML projects.

Day 3: Machine Learning Algorithms & Use Cases

• Topics:
  • Popular algorithms explained (Logistic Regression, Random Forest, SVM, K-means, Neural Networks basics)
  • Choosing the right model for the right problem
  • Real-world use cases:
    • Healthcare: Disease prediction, medical imaging
    • Finance: Fraud detection, credit scoring
    • Retail: Recommendation engines, demand forecasting
    • Manufacturing: Predictive maintenance
  • Hands-on: Building a model for a sample use case (e.g., customer churn prediction)
• Outcome: Participants can identify suitable ML models for real problems.

Day 4: MLOps – ML in Production

• Topics:
  • Introduction to MLOps: Why ML projects fail without it
  • ML pipeline: Data ingestion, model training, testing, deployment, monitoring
  • Tools: MLflow, Kubeflow, TensorFlow Serving, AWS/GCP/Azure ML services
  • CI/CD for ML models
  • Hands-on: Deploy a simple ML model on a cloud platform or Docker
• Outcome: Participants understand how ML models are deployed and managed in production.

Day 5: ML Projects & Future Trends

• Topics:
  • End-to-End ML Project Walkthrough
  • Case Study Discussion (industry-specific examples)
  • Explainable AI (XAI) & Interpretable AI
  • Ethical AI & Responsible AI
  • Future of ML & AI careers
  • Group Project: Teams present a mini ML use case/project
• Outcome: Participants consolidate their learning by presenting a project.

👥 Who Should Attend?

• Beginners in AI & ML
• Data Analysts looking to transition to ML
• IT Professionals & Engineers
• Business Leaders & Managers who want to leverage ML
• Students & Researchers exploring AI/ML

 

Join our 5-day Machine Learning training course designed for beginners, IT professionals, and business leaders. Learn ML fundamentals, data preparation, algorithms, MLOps, and real-world use cases with hands-on projects.

CONTACT – mail@global-skills-institute.com

 

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