Certified Machine Learning Associate

Name: Certified Machine Learning Associate
Rating: 4.5 (4 reviews)

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

Created byKalyanasundaram K

Last updated 5/2025

English

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.

Course content

1 section • 15 lectures • 1h 13m total length

Day 1: Introduction to AI and ML10:12
Day 2: Python for AI/ML6:42
Day 3: Data Preprocessing6:46
Load and clean data, perform exploratory data analysis, handle missing values and outliers, apply feature scaling, normalization, encode categoricals, and split into training and testing sets, using iris data set.
Day 4: Supervised Learning: Linear Regression4:06
Day 5: Supervised Learning: Logistic Regression2:33
Day 6: Supervised Learning: Decision Trees and Random Forest3:29
Learn to use decision trees and random forests on the iris data set, evaluate accuracy score and confusion matrix, apply cross-validation, and visualize trees with matplotlib.
Day 7: Supervised Learning: Support Vector Machines (SVM)3:57
Apply a support vector machine with an rbf kernel to classify iris data, using a standard scaler, then train, predict, evaluate with a confusion matrix and classification report.
Day 8: Unsupervised Learning: K-Means Clustering3:45
Learn k-means clustering to form centroids and split data into k clusters using the iris data set in sklearn. Explore the elbow method, inertia, and centroid visualization.
Day 9: Unsupervised Learning: Principal Component Analysis (PCA)4:29
Day 10: Reinforcement Learning Basics6:35
Day 11: Neural Networks Introduction6:43
Day 12: Deep Learning with TensorFlow/Keras5:29
Explore deep learning with Keras and TensorFlow, building neural networks with layers, activations, and optimizers, applying feature extraction and classification to image, speech, and language tasks.
Day 13: Convolutional Neural Networks (CNNs)3:51
Day 14: Recurrent Neural Networks (RNNs) and LSTMs3:49
Day 15: Capstone Project and Model Deployment0:55

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.

Who this course is for:

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.