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
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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
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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
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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
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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

Calculus is a branch of mathematics focused on understanding change and accumulation. Differentiation deals with the concept of how a quantity changes; it helps in finding the rate of change or the slope of a curve at any given point. This is useful in various real-world applications, such as determining the speed of an object or how a population grows over time. Integration, on the other hand, focuses on accumulation. It is concerned with finding the total size, area, or value when given a rate of change, such as calculating the area under a curve or the total distance traveled given a speed over time. Together, differentiation and integration provide powerful tools to model and solve problems involving dynamic systems in fields like physics, engineering, economics, and beyond.

Calculus : Differentiation and Integration
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