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machine learning

151 tutorials found

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python data analysis data science pandas ai deep learning supervised learning scikit-learn machine-learning expert topics ml foundations core ml model advanced topics module 7 machine learning fundamentals

Data Science(61)

What is Data Science?

Python Data Types and Structures

String and Text Processing

Functions, Lambda, and Comprehensions

File I/O and Data Import

OOP for Data Science

NumPy Essentials

Pandas Series and DataFrames

GroupBy, Merge, and Pivot Tables

Data Cleaning Essentials

Data Cleaning: Missing Values, Outliers and Types

Module 2: NumPy and Pandas

Advanced Pandas: Performance and Patterns

Module 2: NumPy and Pandas

Matplotlib and Seaborn: Data Visualization

Module 3: Visualization

Advanced Visualization: Plotly and Interactive Charts

Module 3: Visualization

Data Storytelling and Communication

Module 3: Visualization

Statistics 101: Mean, Median, Variance and Distributions

Module 4: Statistics and Probability

Probability, Bayes' Theorem and PDF/CDF

Module 4: Statistics and Probability

Statistical Testing: Hypothesis, t-tests and Chi-square

Module 4: Statistics and Probability

A/B Testing and Experimentation

Module 4: Statistics and Probability

Causal Inference: Beyond Correlation

Module 4: Statistics and Probability

SQL for Data Scientists

Module 5: Data Collection and SQL

Web Scraping and API Data Collection

Module 5: Data Collection and SQL

Project 1: EDA on Real Dataset

Module 6: EDA Project

Capstone Foundation Project

Module 6: EDA Project

Introduction to Machine Learning: Supervised, Unsupervised and Reinforcement

Module 7: Machine Learning Fundamentals

Linear Regression: Math, Code and Assumptions

Module 7: Machine Learning Fundamentals

Logistic Regression: Sigmoid, Decision Boundary and Multi-class

Module 7: Machine Learning Fundamentals

Model Evaluation: ROC, AUC, Precision, Recall and F1

Module 7: Machine Learning Fundamentals

Cross-Validation and Bias-Variance Tradeoff

Module 7: Machine Learning Fundamentals

Regularization: Ridge, Lasso and ElasticNet

Module 7: Machine Learning Fundamentals

Decision Trees: Gini, Entropy and Pruning

Module 8: Tree-Based Models

Random Forest: Bootstrap Aggregating and Feature Randomness

Module 8: Tree-Based Models

Gradient Boosting: XGBoost, LightGBM, CatBoost

Module 8: Tree-Based Models

Hyperparameter Tuning: Grid Search, Random Search and Optuna

Module 8: Tree-Based Models

Model Interpretability: SHAP, LIME and Feature Importance

Module 8: Tree-Based Models

K-Means Clustering

Module 2: Machine Learning

PCA: Dimensionality Reduction and Feature Extraction

Module 9: Unsupervised Learning

Anomaly Detection: Isolation Forest, LOF and Autoencoders

Module 9: Unsupervised Learning

Imbalanced Data: SMOTE, Class Weights and Sampling

Module 10: Specialized ML

Time Series Basics: Trend, Seasonality and Stationarity

Module 10: Specialized ML

ARIMA and Prophet: Time Series Forecasting

Module 10: Specialized ML

Recommendation Systems: Collaborative and Content-Based

Module 10: Specialized ML

Graph Data Science: Networks and Graph Analytics

Module 10: Specialized ML

Project 2: End-to-End ML Pipeline

Module 11: End-to-End ML

Neural Networks: Perceptron to MLP — Full Mathematical Foundation

Module 12: Deep Learning

PyTorch Fundamentals: Tensors, Autograd and GPU Computing

Module 12: Deep Learning

Training Loops: Loss Functions, Optimizers and Learning Rate Schedules

Module 12: Deep Learning

CNNs for Image Data: Convolution, Pooling and Architectures

Module 13: Computer Vision

Transfer Learning: Fine-tuning Pre-trained Models

Module 13: Computer Vision

NLP Basics: Tokenization, Embeddings and Word Vectors

Transformers and BERT: Attention Is All You Need

Spark Fundamentals for Big Data

Module 15: Data Engineering and MLOps

Analytics Engineering with dbt

Module 15: Data Engineering and MLOps

MLOps: Experiment Tracking and MLflow

Module 15: Data Engineering and MLOps

Data Versioning with DVC

Module 15: Data Engineering and MLOps

Cloud ML: AWS SageMaker and GCP Vertex AI

Module 15: Data Engineering and MLOps

Model Deployment with FastAPI

Module 15: Data Engineering and MLOps

Project 3: Deploy a Deep Learning Model

Module 16: Deployment Project

Data Science Case Study Prep

Module 17: Career and Portfolio

Building a Data Science Portfolio and GitHub

Module 17: Career and Portfolio

Capstone: End-to-End Data Science Project

Module 17: Career and Portfolio

Machine Learning(50)

What is Machine Learning? — Complete Introduction

Math Foundations for Machine Learning — Linear Algebra, Calculus, Probability

Linear Regression — Complete Guide with Math and Code

Logistic Regression — Complete Guide for Classification

K-Nearest Neighbors — Complete Guide

Decision Trees — Complete Guide with Visualizations

Naive Bayes Classifier — Complete Guide with Visualizations

Support Vector Machines — Complete Guide with Visualizations

Clustering — Complete Guide with Visualizations

Model Evaluation — Complete Guide with Visualizations

Random Forest — Complete Guide for Ensemble Learning

XGBoost and Gradient Boosting — Complete Guide

Feature Engineering — Complete Guide

Dimensionality Reduction — PCA, t-SNE, UMAP Complete Guide

Model Selection and Hyperparameter Tuning Complete Guide

Regularization — Ridge, Lasso and Elastic Net Complete Guide

Ensemble Methods — Bagging, Boosting, Stacking Complete Guide

Time Series Analysis and Forecasting Complete Guide

NLP Fundamentals — Text Processing, Embeddings and Classification

Recommendation Systems — Collaborative and Content-Based Filtering

Neural Networks Fundamentals — Perceptrons to Deep Learning

Convolutional Neural Networks — Complete Guide for Vision

RNN, LSTM and GRU — Sequential Data Complete Guide

GANs — Generative Adversarial Networks Complete Guide

Transformers — Attention Is All You Need Complete Guide

BERT and Encoder Models — Complete Guide

Transfer Learning — Pre-trained Models Complete Guide

Autoencoders — Encoding, Decoding and Representation Learning

Attention Mechanisms — Deep Dive

Training Deep Networks — Optimization, Regularization and Best Practices

GPT Architecture — Decoder-Only Transformers Complete Guide

Advanced Topics

Reinforcement Learning — Complete Guide

Advanced Topics

MLOps — Machine Learning Operations Complete Guide

Advanced Topics

Model Deployment — APIs, Containers and Production ML

Advanced Topics

A/B Testing for ML — Experiment Design and Statistical Rigor

Advanced Topics

ML Ethics — Fairness, Bias, Interpretability and Responsible AI

Advanced Topics

Model Interpretability — SHAP, LIME and Explainable AI

Advanced Topics

Causal Inference — Moving Beyond Correlation

Advanced Topics

Graph Neural Networks — Learning on Graph Structures

Diffusion Models — State-of-the-Art Generative AI

Federated Learning — Privacy-Preserving ML

Meta-Learning — Learning to Learn

Self-Supervised Learning — Pre-training Revolution

AutoML — Automated Machine Learning

ML System Design — Architecture and Production Patterns

Feature Stores — Managing ML Features at Scale

ML Research Methods — Reading Papers and Reproducibility

Capstone Projects — End-to-End ML Applications

ML Interview Prep — Questions, Answers and System Design

ML Cheatsheet — Quick Reference Guide

ml-interview-premium(20)

Linear Regression: Bias-Variance Tradeoff & Regularization (L1/L2)

Machine LearningPremium

Logistic Regression: Decision Boundary, Cost Function & Multiclass

Machine LearningPremium

Decision Trees: Gini vs Entropy, Pruning & Feature Importance

Machine LearningPremium

Random Forest: Bagging, Feature Sampling & Out-of-Bag Error

Machine LearningPremium

Gradient Boosting: XGBoost, LightGBM & CatBoost Deep Dive

Machine LearningPremium

Support Vector Machines: Kernel Trick & Margin Optimization

Machine LearningPremium

K-Nearest Neighbors: Distance Metrics & Curse of Dimensionality

Machine LearningPremium

Naive Bayes: Assumptions, Laplace Smoothing & Text Classification

Machine LearningPremium

Principal Component Analysis: Dimensionality Reduction & Variance Explained

Machine LearningPremium

Feature Engineering: Encoding, Scaling & Feature Selection

Machine LearningPremium

Cross-Validation: K-Fold, Stratified & Time Series Split

Machine LearningPremium

Evaluation Metrics: Precision, Recall, F1, AUC-ROC & Confusion Matrix

Machine LearningPremium

Bias-Variance Tradeoff: Overfitting, Underfitting & Model Complexity

Machine LearningPremium

Regularization: L1, L2, Elastic Net & Dropout

Machine LearningPremium

Ensemble Methods: Bagging, Boosting, Stacking & Voting

Machine LearningPremium

Clustering: K-Means, DBSCAN & Hierarchical

Machine LearningPremium

Dimensionality Reduction: t-SNE, UMAP & Autoencoders

Machine LearningPremium

Time Series: ARIMA, LSTM, Prophet & Stationarity

Machine LearningPremium

Model Deployment: A/B Testing, Model Serving & Drift Detection

Machine LearningPremium

MLOps: Pipeline Automation, Monitoring & CI/CD for ML

Machine LearningPremium

Fintech(6)

Risk Modeling

Credit Scoring

Fraud Detection

Underwriting AI

Alternative Data

Sentiment Trading

Snowflake(3)

Cortex AI: LLM Integration, Cortex Functions & AI Features

Snowflake ML Operations (MLOps)

Snowflake Copilot and AI Functions

PySpark(2)

ML Feature Engineering in PySpark

Model Deployment in PySpark

Python(2)

Python Machine Learning

Python Machine Learning — Getting Started

Python Data Science

dbt(1)

dbt + AI/ML Integration

Drone AI(1)

Machine Learning for Drones

gcp-data-engineering(1)

Vertex AI for ML Pipelines on GCP

Math(1)

Applications in Machine Learning

Information Theory

R(1)

R Machine Learning with caret — Predictive Modeling

spark-interview-premium(1)

Spark ML Pipelines

Apache SparkPremium

Statistics(1)

Statistics Meets Machine Learning

Advanced Statistical Methods