AI for Beginners: Understanding the Basics of Machine Learning
AI for Beginners: Understanding the Basics of Machine Learning is a foundational course that introduces participants to core AI and machine learning concepts. Learners will explore how machines learn from data through practical examples, gaining the skills to apply ML techniques to real-world problems—ideal for those starting their journey in AI.
Ft9051.00 Ft10861.20 17% OFF
/CourseBundles(66478)/4528445-AI.png)
Course Overview
🧠 Why AI for Beginners: Understanding the Basics of Machine Learning?
Artificial Intelligence (AI) is transforming industries, and Machine Learning (ML) is at the heart of this revolution. This course helps beginners grasp the core concepts, workflows, and techniques behind how machines learn from data. It’s ideal for anyone looking to start their journey into AI, data science, or automation.
-
Understand the fundamentals of AI and ML algorithms
-
Learn how models are trained, tested, and deployed
-
Discover real-world applications across industries like finance, healthcare, and technology
🔧 Key Tools & Concepts in AI and Machine Learning:
-
Supervised & Unsupervised Learning – Learn how algorithms find patterns in data
-
Regression, Classification, and Clustering – Core techniques for predictions and insights
-
Neural Networks (Intro) – Understanding how machines mimic the human brain
-
Python & Libraries (NumPy, Pandas, Scikit-learn) – Hands-on coding for ML tasks
-
Data Preprocessing & Feature Engineering – Preparing quality data for accurate models
-
Model Evaluation & Metrics – Measuring and improving model performance
🚀 What You Can Do After This Course:
-
Understand and explain AI and ML concepts confidently
-
Build basic ML models for prediction and classification
-
Analyze and visualize datasets using Python tools
-
Gain the foundation needed to pursue advanced AI and ML learning paths
-
Apply ML principles to real-world problem-solving and decision-making
Course Curriculum
7 Subjects
1. Introduction to Artificial Intelligence
3 Learning Materials
What is AI?
What is AI?-Student Material
History and Evolution of AI
History and Evolution of AI-Student Material
AI vs Machine Learning vs Deep Learning
AI vs Machine Learning vs Deep Learning-Student Material
2. Fundamentals of Machine Learning
3 Learning Materials
What is Machine Learning?
What is Machine Learning?-Student Material
Types of Machine Learning
Types of Machine Learning-Student Material
ML Workflow
ML Workflow-Student Material
3. Data Handling and Preprocessing
3 Learning Materials
Data Collection and Sources
Data Collection and Sources-Student Material
Data Cleaning and Preparation
Data Cleaning and Preparation
Feature Engineering
Feature Engineering-Student Material
4. Supervised Learning Techniques
3 Learning Materials
Regression Analysis
Regression Analysis-Student Material
Classification Algorithms
Classification Algorithms-Student Material
Model Evaluation Metrics
Model Evaluation Metrics-Student Material
5. Unsupervised Learning Techniques
3 Learning Materials
Clustering
Clustering-Student Material
Dimensionality Reduction
Dimensionality Reduction-Study Material
Applications of Unsupervised Learning
Applications of Unsupervised Learning-Student Material
6. Introduction to Neural Networks and Deep Learning
3 Learning Materials
Basics of Neural Networks
Basics of Neural Networks-Study material
Simple Feedforward Networks
Simple Feedforward Networks-Student Material
Real-World Applications
Real-World Applications-Student Material
7. Practical Implementation with Python
3 Learning Materials
Python for Machine Learning
Python for Machine Learning-Student Material
Scikit-learn for ML Models
Scikit-learn for ML Models-Student Material
Model Training and Testing
Model Training and Testing-Student Material
By clicking on Continue, I accept the Terms & Conditions,
Privacy Policy & Refund Policy