"Build strong mathematical foundations required for Machine Learning. This section covers Linear Algebra, Calculus, and Discrete Mathematics with ML-focused explanations and examples.",
keywords: [
"mathematics for machine learning",
"math for ML",
"linear algebra for ML",
"calculus for ML",
"discrete mathematics for ML",
],
},
items: [
// =========================
// Linear Algebra
// =========================
{
type: "category",
label: "Linear Algebra",
link: {
type: "generated-index",
title: "Linear Algebra for Machine Learning",
description:
"Learn core Linear Algebra concepts used in Machine Learning, including vectors, matrices, eigenvalues, and matrix decompositions.",
title: "Probability Distributions for Machine Learning",
description:
"Learn the most important probability distributions used in Machine Learning and Data Science. This section explains Normal, Binomial, Poisson, and Uniform distributions with intuition, formulas, and real-world ML examples.",
title: "Python Basic Syntax for Machine Learning",
description:
"Learn the core Python syntax required for Machine Learning. This section covers variables, data types, control flow, data structures, functions, and exception handling with ML-focused examples.",
title: "Essential Python Libraries for Machine Learning",
description:
"Master the core Python libraries used in Machine Learning and Data Science. Learn NumPy for numerical computing, Pandas for data manipulation, Matplotlib for visualization, and Seaborn for statistical data exploration.",
title: "Data Engineering Basics for Machine Learning",
description:
"Learn how real-world data is collected, stored, cleaned, and prepared for Machine Learning. This section covers data sources, formats, and preprocessing techniques essential for building reliable ML pipelines.",
keywords: [
"data engineering for machine learning",
"data collection",
"data preprocessing",
"data formats",
"feature engineering",
"machine learning pipelines",
],
},
items: [
{
type: "category",
label: "Data Collection",
link: {
type: "generated-index",
title: "Data Collection for Machine Learning",
description:
"Understand where data comes from and how it is collected for Machine Learning systems, including databases, APIs, the internet, mobile apps, and IoT devices.",
"Learn about the most common data formats used in Machine Learning workflows, including tabular, semi-structured, and columnar formats, and understand when to use each.",
"Prepare raw data for Machine Learning by handling missing values, engineering features, scaling data, reducing dimensionality, and selecting the most relevant features.",
"Dive into the core concepts of Machine Learning. Learn how ML works, explore different learning paradigms, implement models using Scikit-learn, and understand evaluation techniques used in real-world ML systems.",
"Understand the different learning paradigms in Machine Learning, including supervised, unsupervised, semi-supervised, self-supervised, and reinforcement learning.",
"Learn how to build end-to-end Machine Learning workflows using Scikit-learn, from data loading and preprocessing to model selection, tuning, and making predictions.",
"Learn supervised learning techniques where models are trained using labeled data. Explore classification and regression algorithms widely used in industry.",
keywords: [
"supervised learning",
"classification algorithms",
"regression algorithms",
"labeled data",
],
},
items: [
{
type: "category",
label: "Classification",
link: {
type: "generated-index",
title: "Classification Algorithms",
description:
"Explore classification algorithms used to predict discrete class labels, including distance-based, probabilistic, margin-based, and tree-based methods.",
"Learn regression techniques used to predict continuous values. Understand linear and regularized regression models and their practical applications.",
"Discover unsupervised learning methods used to uncover hidden patterns in unlabeled data, including clustering and dimensionality reduction techniques.",
keywords: [
"unsupervised learning",
"clustering",
"dimensionality reduction",
"kmeans",
"pca",
],
},
items: [
{
type: "category",
label: "Clustering",
link: {
type: "generated-index",
title: "Clustering Algorithms",
description:
"Learn clustering techniques that group similar data points together, helping in customer segmentation, anomaly detection, and exploratory analysis.",
"Explore Deep Learning from fundamentals to advanced architectures. Learn how neural networks work, use popular frameworks, and build real-world applications using CNNs, RNNs, Transformers, Autoencoders, and GANs.",
keywords: [
"deep learning",
"neural networks",
"cnn",
"rnn",
"transformers",
"autoencoders",
"gans",
"deep learning frameworks",
],
},
items: [
{
type: "category",
label: "Neural Network Basics",
link: {
type: "generated-index",
title: "Neural Network Fundamentals",
description:
"Understand the core building blocks of neural networks, including perceptrons, multilayer networks, forward and backward propagation, activation functions, and loss functions.",
"Learn how CNNs work and why they are powerful for image and video processing tasks. Understand convolution, pooling, padding, and stride operations.",
keywords: [
"cnn",
"convolutional neural networks",
"image processing",
"feature maps",
"pooling",
],
},
items: [
"machine-learning/deep-learning/cnn/convolution",
"machine-learning/deep-learning/cnn/pooling",
"machine-learning/deep-learning/cnn/padding",
"machine-learning/deep-learning/cnn/strides",
],
},
{
type: "category",
label: "CNN Applications",
link: {
type: "generated-index",
title: "Applications of CNNs",
description:
"Explore real-world applications of Convolutional Neural Networks including image classification, segmentation, video recognition, and recommendation systems.",
"Dive into advanced areas of Machine Learning including Natural Language Processing, Explainable AI, MLOps, AI Agents, and the AI Engineer roadmap. Learn how ML systems are built, deployed, monitored, and scaled in production.",
keywords: [
"advanced machine learning",
"nlp",
"explainable ai",
"mlops",
"ai agents",
"ai engineer roadmap",
],
},
items: [
{
type: "category",
label: "Natural Language Processing (NLP)",
link: {
type: "generated-index",
title: "Natural Language Processing",
description:
"Learn how machines understand human language. Explore text preprocessing, embeddings, attention-based models, and modern NLP techniques used in real-world applications.",
"Understand how to interpret and explain machine learning models. Learn why explainability matters and explore popular techniques like LIME and SHAP.",
"Learn how to deploy, monitor, and maintain machine learning models in production. Understand CI/CD pipelines, data versioning, reproducibility, and model monitoring.",
"Explore autonomous AI agents capable of planning, reasoning, and acting. Learn how modern agent systems work and how they integrate with tools, APIs, and LLMs.",