Introduction to the world of AI with Machine Learning

An Introduction to the world of AI with Machine Learning

Learn the Foundations of Machine Learning at Innomatics

The “An Introduction to the world of AI with Machine Learning” course introduces key concepts in machine learning, including data preprocessing, model evaluation, and algorithm selection. It focuses on practical skills for building and assessing machine learning models. At Innomatics, we offer hands-on learning experiences that go beyond theory, with real-world projects and expert mentoring. Our Data Science program covers machine learning fundamentals, advanced techniques, and provides ample opportunities for practice, ensuring you’re prepared for the growing demand in tech roles.

What You’ll Learn:

  • Build machine learning models by following a step-by-step process.
  • Use the right metrics to measure the success of your models for various business issues.
  • Create prediction models using regression and decision trees.
  • Learn how to apply unsupervised machine learning to extract insights from data.
Nasscom Foundation of Machine Learning

Skills You Will Gain

Logistic Regression

Unsupervised Learning

Data Pre-Processing

Linear
Regression

Decision Tree

An Introduction to World of AI with Machine Learning(Syllabus)

Your Title Goes Here

Your content goes here. Edit or remove this text inline or in the module Content settings. You can also style every aspect of this content in the module Design settings and even apply custom CSS to this text in the module Advanced settings.

Introduction to Machine Learning
  • What is Machine Learning?
  • How is it different from conventional Programming?
  • Why  is Machine learning needed ?
    Types of Machine Learning
    • What is Supervised learning? 
    • Unsupervised Learning (Clustering & Dimensionality Reduction)
    • Solving case studies on Classification & Regression tasks
    • Evaluation Metrics
    Data Preparation
    • Numerical Variables
    • Categorical Variables
    • Rescaling Numerical Variables – Standardization and Normalization
    • Encoding Categorical Variables – One Hot Encoding and Ordinal Encoding
    • Hands-on implementation with sklearn Library
      Machine Learning Case study

      End to End Case study