Machine Learning with Python 5+ Hour Video Bundle

+ Welcome! 1 lecture

Submit a Question/Feedback Locked

+ Let's Build It! Data Transformation ▶️ 2 lectures

Overview Locked

Complete Full Course Source Files Locked

+ Label Studio for Data Labeling 📖 2 lectures

What is Label Studio Locked

Label Studio: advanced use cases (multi-modal, collaborative reviews) Locked

+ Tooling Mastery for Manual Labeling 📖 2 lectures

Prodigy: integrating model-in-the-loop corrections Locked

Doccano: custom schema design for NLP annotation Locked

+ Human-Led Annotation Task Design 📖 3 lectures

Decision tree construction for labeling consistency Locked

Multilabel, multi-task, and nested entity annotation schema Locked

Annotation schema AB testing for clarity and reproducibility Locked

+ Assisted Labeling Techniques 📖 3 lectures

Rule-based pre-labeling + human correction Locked

Bootstrapping with weak supervision (Snorkel, skweak, etc.) Locked

Confidence-based filtering for human-in-the-loop escalation Locked

+ Inter-Annotator Agreement (IAA) as a Feedback Signal 📖 2 lectures

Metrics: Cohen’s Kappa, Krippendorff’s Alpha, Fleiss’ Kappa Locked

Conflict resolution workflows and disagreement dashboards Locked

+ Metrics for Label Quality Monitoring 📖 2 lectures

Label drift over time: visualizing consistency decay Locked

Annotator performance analytics (speed, accuracy, disagreement rate) Locked

+ Uncertainty Detection and Annotation Management 📖 2 lectures

Integrate active learning loop with a model to flag uncertain samples Locked

Create review queues, consensus logic, and annotator feedback capture Locked

+ Human-Centric Data Curation at Scale 📖 2 lectures

Strategic search for underrepresented edge cases Locked

Hybrid pipelines: scraping + expert review + augmentation Locked

+ Model Disagreement and Consensus 📖 2 lectures

Leveraging model disagreement (disagreement-as-signal heuristic) Locked

Redundant labeling & consensus modeling Locked

+ Load, clean and encode data ▶️ 4 lectures

01A Load And Clean A Public Dataset Locked

01B What Is One-Hot Encoding Locked

02 Build X And Y Data With One Hot Encoding Locked

03 Logistic Regression With One Hot Encoding Locked

+ Data engineering for machine learning ▶️ 3 lectures

01 Scale And Encode Data With Scikit-Learn Locked

02 What Is Scaling Data Locked

03 Build, Train And Test A Machine Learning Model Locked

+ Build regression and discretizer models ▶️ 6 lectures

01 Compare Decision Tree And Linear Regression Models Locked

02 What Is The Kbins Discretizer Locked

03 Bin Data With Kbins Discretizer Locked

04 Build A Linear Regression Model On Stacked Data Locked

05 What Is K Means Clustering Locked

06 Compare Binned Regression Models Locked

+ Data transformation and feature selection for ridge regression ▶️ 6 lectures

01 Build Univariate Nonlinear Transformatio Locked

02 What Is Gaussian Probability Distribution- Locked

03 What Is Poisson Distribution Locked

04 Build X Y Data With Poisson Distribution In Numpy Locked

05 What Is Logarithmic Data Transformation Locked

06 Build A Ridge Regression Model Locked

+ 00a Foundations of AI App Empires 3 lectures

How billion-dollar apps apply ML to core features Locked

Understanding the data-to-insight lifecycle in ML Locked

Business models that scale with ML Locked

+ 00b AI-Powered Product Patterns 4 lectures

AI-Powered Product Patterns Locked

ML-powered feed ranking (TikTok, Instagram) Locked

Smart assistants & chat Locked

Submit a Question/Feedback Locked

+ 00c Machine Learning Theory Crash Course 12 lectures

Intro to Machine Learning Slides Locked

01 Course Intro Locked

02 Quick Intro To Machine Learning Locked

03 Deep Dive Into Machine Learning Locked

04 Problems Solved With Machine Learning Part 1 Locked

05 Problems Solved With Machine Learning Part 2 Locked

06 Types Of Machine Learning Locked

07 How Machine Learning Works Locked

08 Common Machine Learning Structures Locked

09 Steps To Build A Machine Learning Program Locked

10 Summary And Outro Locked

Fill in the Blank Quiz - Machine Learning Intro Locked

+ 01 Introduction to Machine Learning 12 lectures

01 What Is Machine Learning Locked

02 Types Of Machine Learning Models Locked

03 What Is Supervised Learning Locked

04 What Is Unsupervised Learning Locked

05 How Does A Machine Learning Agent Learn Locked

06 What Is Inductive Learning Locked

07a Performance Of A Machine Learning Algorithm Locked

07b Calculate Error Of Machine Learning Model Locked

08 Handle Noise In Data Locked

09 Powerful Tools With Machine Learning Libraries Locked

10 Gradient Descent Locked

11 Linear Algebra for Machine Learning Locked

+ 01a Regression Models 9 lectures

01 What Is Linear Regression Locked

02 What Is Logistic Regression Locked

03 Prepare Data For Logistic Regression Locked

04a How To Prepare Data Locked

04b How To Build A Logistic Regression Model Locked

04c What Is Optimization Locked

05 How To Optimize A Logistic Regression Model Locked

Quiz - Regression Locked

Source Files Locked

+ 01b Gradient Boosted Classification 5 lectures

01 What Is Gradient Boosting Locked

02 How To Shape Data For Classification Locked

03 How To Build A Boosted Trees Classifier Locked

Source Files Locked

Quiz - Gradient Boosted Classification Locked

+ 02a Deep Learning 3 lectures

01 What Is Deep Learning Locked

02 What Is A Neural Network Locked

Source Files Locked

+ 02b Convolutional Neural Network 5 lectures

01 What is a Convolutional Neural Network Locked

02a What Are Wrappers Locked

03 What is the Adam Optimizer Locked

Mindmap - Structure of a CNN Locked

02bSource Files Locked

+ 02c Layers of a CIFAR-Image Classification Neural Network 5 lectures

01 2D Convolution Layer Locked

02 Relu Activation Function Locked

03 2D Max Pooling Layer Locked

04 Flatten And Dense Layers Locked

Source Files Locked

+ 02d Optimizer and Loss for CIFAR-Image Classification Neural Network 3 lectures

01 How Do You Build An Optimizer For CIFAR-image Classification Locked

02 How Do You Calculate Loss For CIFAR-image Classification Locked

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+ 03 Generative Neural Network Fundamentals 5 lectures

01 What Is A Generative Neural Network Locked

02 What Is A Convolutional Neural Network Locked

03 How To Build A Convolutional Neural Network Locked

04 How Do You Build A Generator Locked

Flashcards - Generative Neural Network Locked

+ 04 Discriminator Neural Network Fundamentals 1 lecture

01 How Do You Build A Discriminator Locked

+ 05 Other Types of Neural Networks 4 lectures

01 How Do You Build A Neural Network For Image Segmentation Locked

02 What Is A Recurrent Neural Network Locked

03 What Is A Dynamic Neural Network Locked

04 What Is A Bi-directional Neural Network Locked

+ 06 Word2Vec Sentiment Classification of Words 5 lectures

01 How Do You Build An Embedding Layer Locked

02 What Is Word2vec Locked

03 How Do You Vectorize Text Locked

04 How Do You Build A Word2vec Sequential Layer Locked

Source Files Locked

+ 07 Learn Image Generation Machine Learning Through Interview Questions 3 lectures

01 What Is Discriminative Modeling Locked

02 What Is Generative Modeling Locked

Interview Source Files Locked

+ 08a Diffusion Deep Learning Interview Questions 4 lectures

01 What Are Diffusion Models Locked

02 How Diffusion Works In Deep Learning Locked

03 Forward And Backward Diffusion In Ml Locked

04 What Is A U-Net Ml Model Locked

+ 08b Stable Diffusion Interview Questions 3 lectures

01 Steps Of Latent Reverse Diffusion In Stable Diffusion Locked

02 What Is Latent Space Locked

03 What Is The Manifold Hypothesis In Ml Locked

+ 09 Dimensionality Reduction Data Science Interview Questions 2 lectures

01 What Is Dimensionality Reduction Locked

02 What Is Principal Component Analysis Locked

+ 10 Autoencoder Machine Learning Interview Questions 5 lectures

01 What Are Autoencoders Locked

02 What Are Encoders And Decoders In Ml Locked

03 How Do Autoencoders Work Locked

04 What Are Variational Autoencoders Locked

05 What Is A Vector Quantized Variational Autoencoder Locked

+ 11 GAN Neural Network Interview Questions 3 lectures

01 What Is The Structure Of A Generative Adversarial Network Locked

02 What Are Discriminators And Generators Locked

03 What Is Zero-Shot Learning Locked

+ 12 Scoring Interview Questions 4 lectures

01 What Is Inception Score Locked

02 What Is Frechet Inception Distance Locked

03 How Fid Works In Ml Locked

04 What Is Kernel Inception Distance Locked

+ 13 Signal Processing Interview Questions 4 lectures

01 What Is Time-Series Data Locked

02 What Is Signal Data Locked

03 Continuous Signals Vs Discrete Signals Locked

04 What Is Nyquist Rate Locked

+ 14 Fourier Analysis Interview Questions 2 lectures

01 What Are Periodic Signals Locked

02 What Is Fourier Transform Locked

+ Where to go from here! Extra Bundled Courses 1 lecture

Extra Courses Locked