Course Content
Introduction to AI and Machine Learning
0/7
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
0/10
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
0/4
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
0/3
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

Linear regression is a statistical technique used to model and analyze the relationship between a dependent variable and one or more independent variables.

Simple linear regression involves a single independent variable and aims to establish a linear relationship between this variable and the dependent variable. It assumes that changes in the independent variable predict changes in the dependent variable in a straight-line manner. For example, it might be used to predict a person’s weight based on their height.

Multiple linear regression, on the other hand, involves two or more independent variables and is used to understand how multiple factors simultaneously influence the dependent variable. This method extends the concept of simple linear regression by incorporating multiple predictors, allowing for a more comprehensive analysis of how various factors interact and contribute to the outcome. For instance, it could be used to predict house prices based on factors like square footage, number of bedrooms, and location.

Both techniques are valuable for identifying trends, making predictions, and understanding relationships between variables, though multiple linear regression offers a more nuanced view by accounting for the impact of several variables at once.

Linear Regression : Simple and Multiple Linear Regression
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