Artificial Intelligence Treasure Adventure: AI Secrets

Focused View

Emiliano Lako

4:01:28

74 View

1 - Introduction Of The Course

1 - Course Structure.mp4

03:25

2 - Linear Algebra For Machine Learning

1 - L2 Norms exercise.html

2 - Vectors and Matrices Scalar Vector Matrix Tensor.mp4

03:19

3 - Vector Operations.mp4

06:11

4 - Matrix Operations.mp4

08:57

5 - Norms in ML.mp4

07:36

6 - Linear Map And Linear Transformation.mp4

09:01

7 - Eigenvalues and Eigenvectors.mp4

05:02

8 - Principal Component Analysis.mp4

04:39

9 - LU Decomposition.mp4

06:48

10 - QR Decomposition and GramSchmid Process.mp4

08:26

3 - Calculus And Optimizations

2 - Derivatives Quiz.html

3 - Integration.html

11 - Basics of Calculus Derivatives and Partial Derivatives.mp4

07:05

12 - Gradients and Directional Derivatives.mp4

03:58

13 - Integration Double Triple integrals.mp4

05:17

14 - Local And Global MinimaMaxima.mp4

05:25

15 - Gradient Descent And Stochastic Gradient Descent.mp4

06:46

16 - Newtons Method And Conjugate Gradient Descent.mp4

04:37

17 - Regularization Techniques L1 L2 Elastic Net.mp4

04:33

4 - Probability and Statistics for Machine Learning

4 - Bayes Theorem.html

18 - Random Variables and Probability Distributions.mp4

05:09

19 - Joint Marginal and Conditional Distribution.mp4

05:58

20 - Hypothesis Testing.mp4

05:09

21 - Confidence Intervals.mp4

04:46

22 - Maximum Likelihood Estimation MLE and Bayesian Estimation.mp4

06:13

23 - Naive Bayes Classifier.mp4

04:52

24 - Gaussian Mixture Models GMMs.mp4

03:58

25 - Hidden Markov Models HMMs.mp4

03:38

5 - Multivariable Calculus and Gradient Based Optimizations

26 - Jacobian Matrices.mp4

03:54

27 - Chain Rule and HighOrder Derivatives.mp4

03:37

28 - Hessian Matrix and secondorder Conditions.mp4

03:44

29 - Backpropagation in Neural Network.mp4

06:22

30 - Vanishing And Exploding Gradients.mp4

06:57

31 - Optimizers Adam RMSProp SGD.mp4

02:24

6 - Linear Regression and Entropy

32 - Least Square Estimation.mp4

05:18

33 - Normal Equations and Matrix Formulations.mp4

03:19

34 - Polynomial Regression.mp4

03:38

35 - Shannon Entropy.mp4

03:32

36 - CrossEntropy Loss.mp4

04:09

37 - Kullback Leibler Divergence.mp4

04:07

7 - Neural Networks

38 - FeedForward Neural Networks FNNs.mp4

04:16

39 - Convolutional Neural Networks CNNs.mp4

04:12

40 - Recurrent Neural Network RNN.mp4

04:25

41 - Graph Theory And NN.mp4

04:33

42 - Autoencoders and Variational Autoencoders.mp4

04:22

43 - Generative Adversarial Networks GANs.mp4

04:04

8 - Advanced Topics in Machine Learning

44 - Image Classification and Object Detection.mp4

05:55

45 - Natural Language Processing NLP.mp4

05:12

46 - Reinforcement Learning.mp4

04:04

47 - Quantum Machine Learning.mp4

04:07

48 - PresentationAI.pdf

48 - Resources and Further Learning.mp4

04:29

Description

Unveiling the Elegance of Machine/Deep Learning

What You'll Learn?

Foundational Understanding of Mathematical Concepts in Machine Learning
Application of Mathematics to Neural Networks
Math-Driven Problem Solving in Deep Learning
Advanced Optimization and Regularization Techniques

Who is this for?

People interested in Machine Learning (With basic programming background)

What You Need to Know?

Basic understanding of algebra, including equations, functions, and basic operations.

Familiarity with basic concepts of calculus, including limits, derivatives, and integrals.

Basic knowledge of probability and statistics, including concepts of probability distributions, mean, and variance.

Basic programming skills in a language like Python, including variables, loops, and functions.

Basic knowledge of machine learning concepts, such as supervised and unsupervised learning.

More details

Description
Delve into the captivating world where mathematics intertwines with the cutting-edge realm of Artificial Intelligence. Welcome to "Heart of AI: Mathematical Marvels in Machine Learning," a meticulously crafted Udemy course that illuminates the profound role of mathematical principles in shaping the landscape of modern machine learning.
Unlock the enigma behind AI algorithms as you embark on a journey that demystifies the complex equations and theorems driving machine learning innovations. Designed for both aspiring and seasoned data enthusiasts, this course transcends mere implementation and guides you through the mathematical core, empowering you to grasp the inner workings of AI models with clarity.
What You'll Learn:
Foundations of Optimization: Discover the beauty of optimization techniques such as gradient descent, Newton's method, and conjugate gradient descent. Gain a deep understanding of how these mathematical marvels underpin the process of fine-tuning AI models for unparalleled performance.
Linear Algebra Mastery: Immerse yourself in the elegant world of linear algebra, where matrices, vectors, and eigenvalues play a pivotal role in expressing and transforming data. Witness the power of linear algebra in crafting neural networks and dimensionality reduction methods.
Probability and Statistics Unveiled: Unravel the secrets of probability distributions, statistical inference, and hypothesis testingâ€”the bedrock of AI's decision-making prowess. Witness the application of these principles in designing Bayesian networks and Gaussian processes.
Functional Analysis in Feature Spaces: Explore the intriguing concept of functional analysis and its implications in feature engineering and kernel methods. Delve into support vector machines, kernel PCA, and other advanced techniques that capitalize on this mathematical foundation.
Real-world Examples and Practical Insights: This course bridges theory and practice seamlessly by infusing every concept with real-world examples and practical insights. From training a neural network to identifying patterns in complex datasets, you'll witness firsthand how the mathematical concepts you learn are translated into tangible AI applications.
Embark on a transformative learning experience guided by engaging lectures, interactive exercises, and captivating case studies. Whether you're an AI enthusiast seeking to unravel the mathematical fabric of machine learning or a professional aiming to fortify your expertise, "Heart of AI: Mathematical Marvels in Machine Learning" is your compass to navigate the intricate terrain of AI's mathematical heart. Enroll now and embark on a journey that deepens your understanding, ignites your curiosity, and empowers you to shape the future of AI.

Who this course is for:
People interested in Machine Learning (With basic programming background)

User Reviews

Rating

average 0

Total votes0

Focused display

Machine Learning

Emiliano Lako

Instructor's Courses

Hi, my name is Emiliano Lako, and I am currently a student pursuing a degree in Electronics Engineering. I have always been passionate about technology and innovation, which is what drew me to the field of engineering in the first place.Throughout my academic journey, I have excelled in subjects such as calculus, physics, and computer programming, demonstrating a natural aptitude for math and science. I have also sought out practical experience through internships and research projects, expanding my knowledge and skills in the field.Outside of my studies, I am an active member of various student organizations related to engineering and technology. I take on leadership roles in these groups, organizing events and initiatives that promote collaboration and innovation among students.After completing my degree, my goal is to pursue a career in the electronics industry. I am passionate about developing cutting-edge technologies that can make a positive impact on society, and I am confident that my technical proficiency, strong work ethic, and dedication to innovation will allow me to make a valuable contribution to the field of engineering.

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.