Machine Learning Fundamentals

About Course

Learn the fundamental concepts of Machine Learning, understand how machines learn from data, and explore the key techniques used to build and evaluate predictive models. This beginner-friendly course introduces supervised and unsupervised learning, data preparation, model training, evaluation, and practical Machine Learning applications.

Understand the fundamentals and key concepts of Machine Learning
Learn how Machine Learning systems use data to identify patterns and make predictions
Understand supervised, unsupervised, and reinforcement learning
Learn the basics of data preparation, features, labels, training, and testing
Explore common Machine Learning algorithms and their practical applications
Understand model evaluation, accuracy, overfitting, and underfitting
Explore real-world Machine Learning applications across different industries
Build a strong foundation for further learning in advanced Machine Learning and AI

Course Content

Module 1: Introduction to Machine Learning
What is Machine Learning? Machine Learning vs Artificial Intelligence History and Evolution of Machine Learning How Machines Learn from Data Types of Machine Learning Real-World Machine Learning Applications

Module 2: Data and Supervised Learning
Understanding Data in Machine Learning Features, Labels, and Datasets Training, Validation, and Test Data Introduction to Regression Introduction to Classification Common Supervised Learning Applications

Module 3: Unsupervised Learning and Model Development
Understanding Unsupervised Learning Clustering and Grouping Data Introduction to Dimensionality Reduction Machine Learning Model Training Feature Selection and Data Preparation Practical Examples of Unsupervised Learning

Module 4: Model Evaluation and Practical Applications
Understanding Model Performance Accuracy, Precision, Recall, and F1 Score Overfitting and Underfitting Improving Machine Learning Models Introduction to Reinforcement Learning Real-World Machine Learning Projects and Applications

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Machine Learning Fundamentals

About Course

What Will You Learn?

Course Content

Module 1: Introduction to Machine Learning
What is Machine Learning? Machine Learning vs Artificial Intelligence History and Evolution of Machine Learning How Machines Learn from Data Types of Machine Learning Real-World Machine Learning Applications

Module 2: Data and Supervised Learning
Understanding Data in Machine Learning Features, Labels, and Datasets Training, Validation, and Test Data Introduction to Regression Introduction to Classification Common Supervised Learning Applications

Module 3: Unsupervised Learning and Model Development
Understanding Unsupervised Learning Clustering and Grouping Data Introduction to Dimensionality Reduction Machine Learning Model Training Feature Selection and Data Preparation Practical Examples of Unsupervised Learning

Module 4: Model Evaluation and Practical Applications
Understanding Model Performance Accuracy, Precision, Recall, and F1 Score Overfitting and Underfitting Improving Machine Learning Models Introduction to Reinforcement Learning Real-World Machine Learning Projects and Applications

Please Rotate Your Device

About Course

What Will You Learn?

Course Content

Module 1: Introduction to Machine Learning What is Machine Learning? Machine Learning vs Artificial Intelligence History and Evolution of Machine Learning How Machines Learn from Data Types of Machine Learning Real-World Machine Learning Applications

Module 2: Data and Supervised Learning Understanding Data in Machine Learning Features, Labels, and Datasets Training, Validation, and Test Data Introduction to Regression Introduction to Classification Common Supervised Learning Applications

Module 3: Unsupervised Learning and Model Development Understanding Unsupervised Learning Clustering and Grouping Data Introduction to Dimensionality Reduction Machine Learning Model Training Feature Selection and Data Preparation Practical Examples of Unsupervised Learning

Module 4: Model Evaluation and Practical Applications Understanding Model Performance Accuracy, Precision, Recall, and F1 Score Overfitting and Underfitting Improving Machine Learning Models Introduction to Reinforcement Learning Real-World Machine Learning Projects and Applications

Student Ratings & Reviews

Please Rotate Your Device

Module 1: Introduction to Machine Learning
What is Machine Learning? Machine Learning vs Artificial Intelligence History and Evolution of Machine Learning How Machines Learn from Data Types of Machine Learning Real-World Machine Learning Applications

Module 2: Data and Supervised Learning
Understanding Data in Machine Learning Features, Labels, and Datasets Training, Validation, and Test Data Introduction to Regression Introduction to Classification Common Supervised Learning Applications

Module 3: Unsupervised Learning and Model Development
Understanding Unsupervised Learning Clustering and Grouping Data Introduction to Dimensionality Reduction Machine Learning Model Training Feature Selection and Data Preparation Practical Examples of Unsupervised Learning

Module 4: Model Evaluation and Practical Applications
Understanding Model Performance Accuracy, Precision, Recall, and F1 Score Overfitting and Underfitting Improving Machine Learning Models Introduction to Reinforcement Learning Real-World Machine Learning Projects and Applications