Home
»
Data Science & AI
»
AI Tools

Comprehensive Guide to AI and Machine Learning Applications

Unlock the Future: Master AI & Machine Learning with NPTEL-IITM’s Comprehensive Course! Dive into Neural Networks, Deep Learning, Probabilities, and Optimization Techniques tailored for Engineering & Science Applications. Your AI journey starts here!

5.0 (19)

142 learners

What you'll learn

Understand the historical development and foundational concepts of artificial intelligence.
Gain proficiency in applying machine learning techniques to engineering and science problems.
Develop skills in using linear algebra and calculus for machine learning modeling.
Learn to implement and optimize machine learning algorithms using Python packages.

This course includes

32 hours of video
Certificate of completion
Access on mobile and TV

Course content

1 modules • 106 lessons • 32 hours of video

Comprehensive Guide to AI and Machine Learning Applications

106 lessons • 32 hours

▶

#1 Introduction to the Course History of Artificial Intelligence44:23
#2 Overview of Machine Learning | Machine Learning for Engineering & Science Applications31:12
#3 Why Linear Algebra ? | Scalars, Vectors, Tensors22:58
#4 Basic Operations | Machine Learning for Engineering & Science Applications14:00
#5 Norms | Machine Learning for Engineering & Science Applications16:37
#6 Linear Combinations | Span Linear Independence11:05
#7 Matrix Operations Special Matrices Matrix Decompositions35:47
#8 Introduction to Probability Theory Discrete & Continuous Random Variables26:50
#9 Conditional | Joint | Marginal Probabilities Sum Rule & Product Rule Bayes' Theorem26:00
#10 Bayes' Theorem | Simple Examples | Machine Learning for Engineering & Science Applications28:11
#11 Independence Conditional Independence Chain Rule Of Probability17:55
#12 Expectation | Machine Learning for Engineering & Science Applications16:17
#13 Variance Covariance | Machine Learning for Engineering & Science Applications26:04
#14 Some Relations for Expectation & Covariance | Slightly Advanced14:05
#15 Machine Representation of Numbers | Overflow | Underflow | Condition Number37:44
#16 Derivatives | Gradient | Hessian | Jacobian | Taylor Series21:32
#17 Matrix Calculus | Slightly Advanced | Machine Learning for Engineering & Science Applications16:55
#18 Optimization | Part 1 | Unconstrained Optimization12:35
#19 Introduction to Constrained Optimization | Unconstrained Optimization08:18
#20 Introduction to Numerical Optimization Gradient Descent | Part 122:53
#21 Gradient Descent | Part 2 | Proof | Numerical Gradient | Stopping Criteria15:38
#22 Introduction to Packages | Machine Learning for Engineering & Science Applications16:05
#23 The Learning Paradigm | Machine Learning for Engineering & Science Applications26:15
#24 A Linear Regression Example | Machine Learning for Engineering & Science Applications11:00
#25 Linear Regression | Least Squares | Gradient Descent21:52
#26 Coding Linear Regression | Machine Learning for Engineering & Science Applications11:42
#27 Generalized Function for Linear Regression16:43
#28 Goodness of Fit | Machine Learning for Engineering & Science Applications10:45
#29 Bias Variance Trade Off | Machine Learning for Engineering & Science Applications26:18
#30 Gradient Descent Algorithms | Machine Learning for Engineering & Science Applications12:05
#31 Introduction to Week 5 | Deep Learning | Machine Learning for Engineering & Science Applications12:30
#32 Logistic Regression | Machine Learning for Engineering & Science Applications12:15
#33 Binary Entropy Cost Function | Machine Learning for Engineering & Science Applications12:52
#34 OR Gate Via Classification | Machine Learning for Engineering & Science Applications10:59
#35 NOR | AND | NAND Gates | Machine Learning for Engineering & Science Applications05:35
#36 XOR Gate | Machine Learning for Engineering & Science Applications19:32
#37 Differentiating the Sigmoid | Machine Learning for Engineering & Science Applications02:19
#38 Gradient of Logistic Regression | Machine Learning for Engineering & Science Applications24:34
#39 Code for Logistic Regression | Machine Learning for Engineering & Science Applications05:40
#40 Multinomial Classification | Introduction04:29
#41 Multinomial Classification | One Hot Vector06:10
#42 Multinomial Classification | Softmax | Machine Learning for Engineering & Science Applications17:29
#43 Schematic of Multinomial Logistic Regression08:28
#44 Biological Neuron | Machine Learning for Engineering & Science Applications07:49
#45 Structure of an Artificial Neuron | Machine Learning for Engineering & Science Applications04:53
#46 Feedforward Neural Network | Machine Learning for Engineering & Science Applications08:28
#47 Introduction to Back Prop | Machine Learning for Engineering & Science Applications38:12
#48 Summary of Week 05 | Machine Learning for Engineering & Science Applications10:39
#49 Introduction to Convolution Neural Networks (CNN)51:25
#50 Types of Convolution | Machine Learning for Engineering & Science Applications13:22
#51 CNN Architecture | Part 1 | LeNet & Alex Net21:07
#52 CNN Architecture | Part 2 | VGG Net | Machine Learning for Engineering & Science Applications10:44
#53 CNN Architecture | Part 3 | GoogleNet | Machine Learning for Engineering & Science Applications19:51
#54 CNN Architecture | Part 4 | ResNet | Machine Learning for Engineering & Science Applications12:35
#55 CNN Architecture | Part 5 | DenseNet | Machine Learning for Engineering & Science Applications17:16
#56 Train Network for Image Classification | Machine Learning for Engineering & Science Applications22:45
#57 Semantic Segmentation | Machine Learning for Engineering & Science Applications34:11
#58 Hyperparameter Optimization | Machine Learning for Engineering & Science Applications11:41
#59 Transfer Learning | Machine Learning for Engineering & Science Applications16:16
#60 Segmentation of Brain Tumors from MRI using Deep Learning38:50
#61 Activation Functions | Machine Learning for Engineering & Science Applications07:49
#62 Learning Rate Decay | Weight Initialization15:35
#63 Data Normalization | Machine Learning for Engineering & Science Applications11:58
#64 Batch Norm | Machine Learning for Engineering & Science Applications16:27
#65 Introduction to RNNs | Machine Learning for Engineering & Science Applications39:11
#66 Example | Sequence Classification | Machine Learning for Engineering & Science Applications33:13
#67 Training RNNs | Loss & BPTT | Machine Learning for Engineering & Science Applications29:40
#68 Vanishing Gradients & TBPTT | Machine Learning for Engineering & Science Applications25:37
#69 RNN Architectures | Machine Learning for Engineering & Science Applications30:01
#70 LSTM | Machine Learning for Engineering & Science Applications13:25
#71 Why LSTM Works? | Machine Learning for Engineering & Science Applications05:28
#72 Deep RNNs & Bi RNNs | Machine Learning for Engineering & Science Applications14:19
#73 Summary of RNNs | Machine Learning for Engineering & Science Applications04:16
#74 Introduction | Machine Learning for Engineering & Science Applications02:27
#75 Knn | Machine Learning for Engineering & Science Applications10:57
#76 Binary Decision Trees | Machine Learning for Engineering & Science Applications25:27
#77 Binary Regression Trees | Machine Learning for Engineering & Science Applications15:23
#78 Bagging | Machine Learning for Engineering & Science Applications22:17
#79 Random Forest | Machine Learning for Engineering & Science Applications06:40
#80 Boosting | Machine Learning for Engineering & Science Applications34:04
#81 Gradient Boosting | Machine Learning for Engineering & Science Applications18:38
#82 Unsupervised Learning & Kmeans | Machine Learning for Engineering & Science Applications25:31
#83 Agglomerative Clustering | Machine Learning for Engineering & Science Applications18:32
#84 Probability Distributions | Gaussian | Bernoulli32:05
#85 Covariance Matrix of Gaussian Distribution03:40
#86 Central Limit Theorem | Machine Learning for Engineering & Science Applications04:09
#87 Naive Bayes | Machine Learning for Engineering & Science Applications28:36
#88 MLE Intro | Machine Learning for Engineering & Science Applications09:20
#89 PCA | Part 1 | Machine Learning for Engineering & Science Applications04:19
#90 PCA | Part 2 | Machine Learning for Engineering & Science Applications10:11
#91 Support Vector Machines | Machine Learning for Engineering & Science Applications16:42
#92 MLE | MAP & Bayesian Regression | Machine Learning for Engineering & Science Applications24:44
#93 Introduction to Generative Model | Machine Learning for Engineering & Science Applications20:15
#94 Generative Adversarial Networks (GAN) | Machine Learning for Engineering & Science Applications28:44
#95 Variational Auto Encoders (VAE) | Machine Learning for Engineering & Science Applications29:32
#96 Applications | Cardiac MRI | Segmentation & Diagnosis37:08
#97 Applications | Cardiac MRI Analysis | Tensorflow Code Walkthrough22:55
#98 Introduction to Week 12 | Machine Learning for Engineering & Science Applications10:30
#99 Application | Part 1 | Description | Fin Heat Transfer10:48
#100 Application | Part 1 | Solution | Description | Fin Heat Transfer14:14
#101 Application | Part 2 | Description | Computational Fluid Dynamics15:32
#102 Application | Part 2 | Solution | Machine Learning for Engineering & Science Applications23:25
#103 Application | Part 3 | Description | Topology Optimization04:30
#104 Application | Part 3 | Solution | Machine Learning for Engineering & Science Applications07:15
#105 Application | Part 4 | Solution of PDE/ODE using Neural Networks30:57
#106 Summary & Road Ahead | Machine Learning for Engineering & Science Applications14:25

Machine Learning

Neural Networks

Deep Learning

Optimization Techniques

You may also be interested in

Comprehensive Machine Learning Course: Math, Python, AI

Master Machine Learning: Dive Deep with Scikit-Learn, Math, and Python to Unlock AI Potential!

⭐ 4.0

13 ratings

4 hours

Comprehensive Data Science and Python Essentials Course 2025

Empower Your Data Skills: Master Python, SQL & Machine Learning with Intellipaat's Free Beginner-Friendly Course!

⭐ 5.0

19 ratings

59.3 hours

Mastering Data Science: From Basics to Advanced Techniques

Master Data Science: Your Comprehensive Guide to Python, SQL, Machine Learning & Deep Learning! Explore step-by-step tutorials and projects, from coding fundamentals to advanced algorithms, with codebasics. Unlock your data-driven career today!

⭐ 4.6

4 ratings

34 hours

Mastering Affiliate Marketing: Strategies for 2025 Success

Unlock Massive Earnings: Master Affiliate Marketing with Proven Strategies and Real Success Stories!

⭐ 4.4

9 ratings

3 hours

Master Data Science: Beginner's Guide to Success

Unlock the Power of Data: Dive into the Essentials of Data Science with Simplilearn! Perfect for Beginners – Learn Key Concepts, Techniques, and Career Insights to Launch Your Data Science Journey Today!

⭐ 4.2

9 ratings

197 hours