Course Content
Introduction to AI and Machine Learning
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Overview of AI and ML – History and evolution of AI
00:00
Differences between AI, ML, and Data Science
00:00
Types of Machine Learning : Supervised Learning
00:00
Unsupervised Learning
00:00
Reinforcement Learning
00:00
Semi-supervised Learning
00:00
Applications of AI/ML
00:00
Mathematics for Machine Learning
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Linear Algebra : Vectors, Matrices, and Tensors
00:00
Eigenvalues and Eigenvectors
00:00
Singular Value Decomposition
00:00
Calculus : Differentiation and Integration
00:00
Partial Derivatives and Gradient Descent
00:00
Probability and Statistics : Probability Theory
00:00
Distributions (Normal, Binomial, etc.)
00:00
Bayes’ Theorem
00:00
Optimization Techniques : Cost Function
00:00
Optimization Algorithms
00:00
Programming for AI/ML
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Introduction to Python : Python basics and libraries
00:00
Data Handling : Numpy, Pandas, Matplotlib
00:00
Data Preprocessing and Cleaning
00:00
Introduction to Jupyter Notebooks
00:00
Supervised Learning
0/9
Linear Regression : Simple and Multiple Linear Regression
00:00
Evaluation Metrics
00:00
Classification Algorithms : Logistic Regression
00:00
Decision Trees
00:00
Support Vector Machines (SVM)
00:00
k-Nearest Neighbors (k-NN)
00:00
Model Evaluation : Confusion Matrix
00:00
Precision, Recall, F1-Score
00:00
ROC-AUC Curve
00:00
Unsupervised Learning
0/6
Clustering : k-Means Clustering
00:00
Hierarchical Clustering
00:00
DBSCAN
00:00
Dimensionality Reduction : Principal Component Analysis (PCA)
00:00
t-SNE
00:00
LDA
00:00
Neural Networks and Deep Learning
0/7
Introduction to Neural Networks : Perceptron Model
00:00
Activation Functions
00:00
Deep Learning : Multi-Layer Perceptrons (MLP)
00:00
Backpropagation
00:00
Optimization Techniques (Adam, RMSProp)
00:00
Convolutional Neural : Networks (CNNs) CNN Architecture
00:00
Applications in Image Processing
00:00
Reinforcement Learning
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Introduction to Reinforcement Learning : Markov Decision Processes (MDPs)
00:00
Q-Learning and Deep Q-Networks (DQN)
00:00
Applications in Gaming and Robotics
00:00
AI/ML Model Deployment
0/3
Model Deployment Techniques : Flask, Django, FastAPI
00:00
Cloud Services : AWS, Google Cloud, Microsoft Azure
00:00
Monitoring and Scaling ML Models
00:00
AI/ML
About Lesson

Image processing has a wide range of applications across various fields due to its ability to enhance, analyze, and manipulate digital images. In medical imaging, it plays a crucial role in improving the quality of scans such as X-rays, MRIs, and CT scans, allowing for more accurate diagnosis and treatment planning. In the field of security, image processing is fundamental in facial recognition systems, surveillance, and biometrics, helping to identify individuals and detect suspicious activities.

In addition, image processing is extensively used in computer vision applications, such as self-driving cars, where it helps the vehicle perceive its surroundings by detecting objects, recognizing traffic signs, and understanding road conditions. It is also pivotal in digital photography and social media, where it enhances image quality, corrects color, and applies filters to create visually appealing content. In agriculture, image processing assists in monitoring crop health and detecting diseases by analyzing images captured by drones and satellites.

Overall, image processing enables the extraction of valuable information from visual data, making it an essential tool in industries ranging from healthcare and security to entertainment and agriculture.

Applications in Image Processing
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