Certified Machine Learning Associate

Master Machine Learning with hands-on projects covering Supervised, Unsupervised, Deep Learning.

What you'll learn

Gain foundational understanding of AI and machine learning concepts, algorithms, and applications.

Develop practical skills in Python, data preprocessing, and implementing supervised and unsupervised learning models.

Build and train deep learning models including CNNs and RNNs using TensorFlow/Keras.

Complete a capstone project and deploy machine learning models for real-world use cases.

Requirements

Basic computer literacy and willingness to learn programming. No prior experience in AI or machine learning required; all key concepts and tools will be introduced step-by-step. A computer with internet access to install Python and libraries like Anaconda and TensorFlow.

Description

Are you ready to launch your career in one of the most in-demand tech domains? The Certified Machine Learning Associate course is designed for beginners and intermediate learners who want to build a solid foundation in machine learning through a practical, hands-on approach. In this course, you’ll learn: The fundamentals of Supervised and Unsupervised Learning (Linear Regression, Classification, Clustering, PCA) Advanced techniques using Neural Networks, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) Essential algorithms like K-Means, Q-Learning, and Backpropagation Real-world problem solving through capstone projects, such as Smart Agriculture AI for disease detection We use Python along with popular libraries like scikit-learn, TensorFlow, and Keras to help you build, evaluate, and deploy machine learning models. By the end of this course, you’ll have not only theoretical knowledge but also practical experience in solving real-world problems using AI. You'll also learn how to evaluate model performance using precision, recall, and confusion matrices. The course includes interactive quizzes, assignments, and real datasets to ensure deep understanding. By completing the final capstone project, you'll gain the confidence to apply ML in practical scenarios or research.Whether you're a student, aspiring data scientist, or software engineer, this course will help you become job-ready with portfolio-worthy projects and a certificate to validate your skills.

Overview

Section 1: Associate

Lecture 1 Day 1: Introduction to AI and ML

Lecture 2 Day 2: Python for AI/ML

Lecture 3 Day 3: Data Preprocessing

Lecture 4 Day 4: Supervised Learning: Linear Regression

Lecture 5 Day 5: Supervised Learning: Logistic Regression

Lecture 6 Day 6: Supervised Learning: Decision Trees and Random Forest

Lecture 7 Day 7: Supervised Learning: Support Vector Machines (SVM)

Lecture 8 Day 8: Unsupervised Learning: K-Means Clustering

Lecture 9 Day 9: Unsupervised Learning: Principal Component Analysis (PCA)

Lecture 10 Day 10: Reinforcement Learning Basics

Lecture 11 Day 11: Neural Networks Introduction

Lecture 12 Day 12: Deep Learning with TensorFlow/Keras

Lecture 13 Day 13: Convolutional Neural Networks (CNNs)

Lecture 14 Day 14: Recurrent Neural Networks (RNNs) and LSTMs

Lecture 15 Day 15: Capstone Project and Model Deployment

Beginners and aspiring data scientists who want a comprehensive introduction to AI/ML. Developers and professionals looking to upskill in machine learning techniques and deep learning frameworks. Students and tech enthusiasts eager to build hands-on AI/ML projects and gain industry-relevant skills.