Heart Of Ai: A Theoretical Odyssey On Machine Learning

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

Requirements

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.

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 testing—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.

Overview

Section 1: Introduction Of The Course

Lecture 1 Course Structure

Section 2: Linear Algebra For Machine Learning

Lecture 2 Vectors and Matrices (Scalar, Vector, Matrix, Tensor)

Lecture 3 Vector Operations

Lecture 4 Matrix Operations

Lecture 5 Norms in ML

Lecture 6 Linear Map And Linear Transformation

Lecture 7 Eigenvalues and Eigenvectors

Lecture 8 Principal Component Analysis

Lecture 9 LU Decomposition

Lecture 10 QR Decomposition and Gram-Schmid Process

Section 3: Calculus And Optimizations

Lecture 11 Basics of Calculus , Derivatives and Partial Derivatives

Lecture 12 Gradients and Directional Derivatives

Lecture 13 Integration … Double / Triple integrals

Lecture 14 Local And Global Minima/Maxima

Lecture 15 Gradient Descent And Stochastic Gradient Descent

Lecture 16 Newton's Method And Conjugate Gradient Descent

Lecture 17 Regularization Techniques ( L1, L2 , Elastic Net )

Section 4: Probability and Statistics for Machine Learning

Lecture 18 Random Variables and Probability Distributions

Lecture 19 Joint , Marginal and Conditional Distribution

Lecture 20 Hypothesis Testing

Lecture 21 Confidence Intervals

Lecture 22 Maximum Likelihood Estimation ( MLE ) and Bayesian Estimation

Lecture 23 Naive Bayes Classifier

Lecture 24 Gaussian Mixture Models (GMMs)

Lecture 25 Hidden Markov Models (HMMs)

Section 5: Multivariable Calculus and Gradient - Based Optimizations

Lecture 26 Jacobian Matrices

Lecture 27 Chain Rule and High-Order Derivatives

Lecture 28 Hessian Matrix and second-order Conditions

Lecture 29 Backpropagation in Neural Network

Lecture 30 Vanishing And Exploding Gradients

Lecture 31 Optimizers ( Adam, RMSProp , SGD)

Section 6: Linear Regression and Entropy

Lecture 32 Least Square Estimation

Lecture 33 Normal Equations and Matrix Formulations

Lecture 34 Polynomial Regression

Lecture 35 Shannon Entropy

Lecture 36 Cross-Entropy Loss

Lecture 37 Kullback - Leibler Divergence

Section 7: Neural Networks

Lecture 38 FeedForward Neural Networks ( FNNs)

Lecture 39 Convolutional Neural Networks (CNNs)

Lecture 40 Recurrent Neural Network ( RNN)

Lecture 41 Graph Theory And NN

Lecture 42 Autoencoders and Variational Autoencoders

Lecture 43 Generative Adversarial Networks ( GANs)

Section 8: Advanced Topics in Machine Learning

Lecture 44 Image Classification and Object Detection

Lecture 45 Natural Language Processing (NLP)

Lecture 46 Reinforcement Learning

Lecture 47 Quantum Machine Learning

Lecture 48 Resources and Further Learning

People interested in Machine Learning (With basic programming background)