Published 9/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.52 GB | Duration: 3h 58m
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)
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