E-Books →Basic Mathematical Foundations of AI Hands on with Python (Mastering Machine Learning)

With clear explanations and practical examples, each chapter focuses on a specific topic and includes Python code and multiple-choice review questions to reinforce your understanding. From linear equations to advanced techniques like deep learning and reinforcement learning, you will gain a solid understanding of the mathematical concepts essential for working in the field of AI.
In this comprehensive book, you will learn:
- Solving systems of linear equations using matrix methods.
- Exploring quadratic equations and their applications in optimization.
- Finding eigenvalues and eigenvectors for feature extraction.
- Decomposing matrices using algorithms like LU Decomposition and SVD.
- Implementing gradient descent for linear regression and neural networks.
- Estimating linear regression parameters using Ordinary Least Squares (OLS).
- Modeling binary outcomes with logistic regression.
- Applying the softmax function for multiclass classification.
- Understanding various activation functions and their role in neural networks.
- Training neural networks using backpropagation.
- Classifying data using K-Nearest Neighbors and decision trees.
- Harnessing the power of ensemble methods with random forests.
- Maximizing margins with support vector machines.
- Reducing dimensionality with principal component analysis (PCA).
- Discriminating between classes using linear discriminant analysis (LDA).
- Applying probabilistic classification with Naive Bayes.
- Clustering data points using K-Means and Gaussian Mixture Models.
- Modeling sequential data with Hidden Markov Models and Markov Chains.
- Applying filtering algorithms like Particle Filters and Kalman Filters.
- Extracting features with convolutional neural networks (CNN).
- Modeling sequence data with recurrent neural networks (RNN) and LSTMs.
- Enhancing RNNs with attention mechanisms and transformer networks.
- Exploring generative modeling with autoencoders, VAEs, and GANs.
- Mastering reinforcement learning basics, Q-learning, and actor-critic methods.
- Understanding Bayesian inference, Gaussian processes, and MCMC methods.
- Solving complex optimization problems with linear programming, integer programming, and convex optimization.
- Implementing dynamic programming, A* search algorithm, and PageRank.
- Analyzing text data with Latent Dirichlet Allocation (LDA) and matrix factorization.
- Preventing overfitting and regularization techniques like ridge regression and lasso regression.
- Optimizing non-linear functions with Newton's method and stochastic gradient descent.
- Implementing batch gradient descent and mini-batch gradient descent.
- Applying regularization techniques like dropout and batch normalization.
- Boosting weak classifiers with AdaBoost and gradient boosting machines (GBM).
- Visualizing high-dimensional data with t-SNE dimensionality reduction.