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Machine Learning for AI

Machine Learning for AI 03 WEEKS AI
Lesson 8 Students 60+ Beginner

Machine Learning for AI

Course Overview

Machine Learning for AI is designed to build a strong practical foundation in designing, training, and optimizing machine learning models. You will cover core algorithms—regression, classification, clustering, ensemble methods—along with neural networks using Python libraries like Scikit-Learn, TensorFlow, and PyTorch.

Course Description

Master supervised and unsupervised learning, model evaluation, deep neural networks, and algorithmic problem solving.

Live instructor-led training
Hands-on practice sessions
Career guidance support
Course Details
Course Prerequisites
  • Familiarity with Python programming.
  • Basic knowledge of linear algebra and statistics.
Target Audience
  • Aspiring Machine Learning Engineers and Data Scientists.
  • Software developers transitioning into applied AI roles.
  • Data analysts looking to advance into predictive modeling.
What You Will Learn
  • Implement supervised learning models (Regression, Decision Trees, SVMs, Ensembles).
  • Apply unsupervised techniques like K-Means clustering and dimensionality reduction.
  • Conduct exploratory data analysis, data pre-processing, and feature engineering.
Download Curriculum

Download the course curriculum PDF or contact us for the complete training plan.

  • Course introduction
  • Practical sessions
  • Project and career guidance
Course Syllabus
  • Course introduction
  • Practical sessions
  • Project and career guidance
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