Learn · ACM India – RBCDSAI Summer School on DS/AI/ML (For Women Only) Hosted by PSG College of Technology, Coimbatore
Lecture 3 of 82week-01
Introduction to Probability; Introduction to machine learning Part 2
IIT Madras
Credits and licence
- From
- ACM India – RBCDSAI Summer School on DS/AI/ML (For Women Only) Hosted by PSG College of Technology, Coimbatore, IIT Madras (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- Tweens profiles and older. How courses are placed
All the lectures
- Introduction to AI/ML/DS
- Introduction to Probability; Introduction to machine learning Part 1
- Introduction to Probability; Introduction to machine learning Part 2
- Introduction to Probability; Introduction to machine learning Part 3
- Introduction to Probability; Introduction to machine learning Part 4
- Python for AI/ML/DS Part 1
- Python for AI/ML/DS Part 2
- Descriptive statistics and Inferential statistics Part 1
- Descriptive statistics and Inferential statistics Part 2
- Descriptive statistics and Inferential statistics Part 3
- Descriptive statistics and Inferential statistics Part 4
- Descriptive statistics and Inferential statistics Part 5
- Distribution, Data visualization, Plotting libraries Part 1
- Distribution, Data visualization, Plotting libraries Part 2
- Distribution, Data visualization, Plotting libraries Part 3
- Linear Algebra for Data science
- Identification of linear relationship among attributes
- Solving Linear Equations 1
- Solving Linear Equations 2
- Linear Algebra - Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors Part 1
- Linear Algebra - Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors Part 2
- Linear Algebra Part 1
- Linear Algebra Part 2
- Linear Algebra Part 3
- Regression Models, Models Selection & Evaluation Part 1
- Regression Models, Models Selection & Evaluation Part 2
- Regression Models, Models Selection & Evaluation Part 3
- Regression Models, Models Selection & Evaluation Part 4
- Regression Part 1
- Regression Part 2
- Regression Part 3
- Classification Naive Bayes, Logistic Regression, K-NN Part 1
- Classification Naive Bayes, Logistic Regression, K-NN Part 2
- Classification Naive Bayes, Logistic Regression, K-NN Part 3
- Classification Naive Bayes, Logistic Regression, K-NN Part 4
- Classification - Part 1
- Classification - Part 2
- Classification - Part 3
- Linear Models for Classification Part 1
- Linear Models for Classification Part 2
- Kernel Machines
- Solving Langrange Dual in SVM
- Classification and SVM Part 1
- Classification and SVM Part 2
- Tree - Based methods, Boosting bagging Part 1
- Tree - Based methods, Boosting bagging Part 2
- Tree - Based methods, Boosting bagging Part 3
- Tree - Based methods, Boosting bagging Part 4
- Tree-based approaches for regression and classification Part 1
- Tree-based approaches for regression and classification Part 2
- Supervised Learning Using K Nearest Neighbors Part 1
- Supervised Learning Using K Nearest Neighbors Part 2
- Supervised Learning Using K Nearest Neighbors Part 3
- Supervised Learning Using K Nearest Neighbors Part 4
- Clustering methods Part 1
- Clustering methods Part 2
- Induction to Neural Networks, Perceptrons, Multilayer Perceptrons, Feedforward Neural Networks Part1
- Induction to Neural Networks, Perceptrons, Multilayer Perceptrons, Feedforward Neural Networks Part2
- Induction to Neural Networks, Perceptrons, Multilayer Perceptrons, Feedforward Neural Networks Part3
- Induction to Neural Networks, Perceptrons, Multilayer Perceptrons, Feedforward Neural Networks Part4
- Neural Networks and Feedforward NN Part 1
- Neural Networks and Feedforward NN Part 2
- Neural Networks and Feedforward NN Part 3
- Backpropagation (Intuition)
- Backpropagation: Computing Cradients w.r.t the Output Units
- Learning Parameters: Gradient Descent
- Contours
- Nesterov Accelerated Gradient Descent
- Stochastic and Mini-Batch Gradient Descent
- Tips for Adjusting learning Rate and Momentum
- Line Search
- The convolution operation
- Convolutional Neural Networks
- CNN and DL models Part 1
- CNN and DL models Part 2
- CNN and DL models Part 3
- CNN and DL models Part 4
- AI/ML/DS Industry Use Cases Part 1
- AI/ML/DS Industry Use Cases Part 2
- AI/ML - Case Studies in Industry Part 1
- AI/ML - Case Studies in Industry Part 2
- "Q&A on career in research a woman faculty representative from PSGTech and RBCDSAI "