World’s No.1 Affordable Cloud & AI Platform — From Zero to Expert
Courses Images

Course Overview

Master the essentials of machine learning in this training program for the Machine Learning Engineer – Associate exam. The course covers data ingestion, transformation, feature engineering, and bias handling, followed by training, tuning, evaluation, and deployment using SageMaker. Learners also explore CI/CD pipelines, infrastructure as code, monitoring, cost optimization, and security best practices. Real-world case studies focus on end-to-end ML pipelines from data prep to governance. The program ends with practice labs, a mock exam, and final review for certification readiness.

Note: We will use the TensorFlow deep learning framework for programming. A two-day introductory Python class will be conducted, focusing on its use in Computer Vision.

Course Content

  • Problem Definition ( Any one Algorithm will be used)
  • Data Collection
  • Data Preprocessing
  • Feature Engineering
  • EDA
  • Model Selection
  • Model Evaluation
  • Model hyper parameter tuning
  • Usases of various model
  • Selection of best model
  • Deployment of model
  • Model monitoring
  • Analysis of Data Drifts
  • Model Retraining

1: Do I need any experience?

Absolutely! Many AI courses are designed specifically for beginners. They start with foundational concepts and often use beginner-friendly tools and platforms that minimize complex coding. Look for courses labeled "Introductory," "No-code AI," or "AI for Everyone." These will help you build a solid understanding of the principles before you advance to more technical, programming-heavy topics.
These terms are often used interchangeably, but they represent a hierarchy of concepts:
- Artificial Intelligence (AI) is the broadest field, focused on creating machines capable of intelligent behavior.
- Machine Learning (ML) is a subset of AI that gives computers the ability to learn from data without being explicitly programmed for every task.
- Deep Learning (DL) is a further subset of ML that uses complex neural networks to solve advanced problems like image and speech recognition.
Most foundational courses will cover the relationship between these fields, while specialized courses will dive deep into one area.
Our courses are project-based to ensure you gain hands-on experience. You'll work on real-world projects such as building a movie recommendation system, developing a chatbot, or creating an image classifier. These projects form a practical portfolio that demonstrates your skills to employers. This hands-on approach prepares you for roles like AI Specialist, Machine Learning Engineer, or Data Analyst, and equips you with in-demand skills to solve business problems.

Student Reviews

Considering primary motivation for the generation of narratives is a useful concept