Course Content
Introduction to Machine Learning
0/2
What is Machine Learning?
Types of Machine Learning
Python for Machine Learning
0/2
Setting Up the Environment
Basic Python for Data Science
Data Preprocessing
0/3
Data Collection and Importing
Data Cleaning
Feature Scaling and Encoding
Exploratory Data Analysis (EDA)
0/2
Visualizing Data
Statistical Analysis
Supervised Learning
0/3
Linear Regression
Classification Algorithms
Model Evaluation Metrics
Unsupervised Learning
0/2
Clustering Algorithms
Dimensionality Reduction
Advanced Machine Learning Techniques
0/2
Ensemble Learning
Support Vector Machines (SVM)
Neural Networks and Deep Learning
0/4
Introduction to Neural Networks
Deep Learning Frameworks
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Model Deployment and Monitoring
0/2
Model Deployment Techniques
Monitoring and Maintenance
Real-World Projects and Case Studies
0/2
Industry Applications
Capstone Project
Artificial Intelligence Syllabus
Introduction to Artificial Intelligence
0/2
What is Artificial Intelligence?
Branches of AI
Python for AI
0/2
Setting Up the Environment
Basic Python Programming
Problem Solving and Search Algorithms
0/2
Problem-Solving Techniques
Search Algorithms
Knowledge Representation and Reasoning
0/2
Logical Reasoning
Knowledge Representation Techniques
Natural Language Processing (NLP)
0/4
Introduction to NLP
Text Preprocessing
NLP Techniques
Sentiment Analysis and Text Classification
Computer Vision
0/3
Introduction to Computer Vision
Image Processing Techniques
Object Detection and Recognition
Machine Learning in AI
0/2
Role of Machine Learning in AI
Advanced ML Techniques for AI
Robotics and AI
0/1
AI in Robotics
Ethics and Future of AI
0/2
Ethical Considerations in AI
Future Trends in AI
Real-World Applications and Projects
0/2
Industry Applications of AI
Capstone Project
Machine Learning and Artificial Intelligence (Combined Intermediate & Advanced Level)
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