AI & Machine Learning in Excel – PDF Workbooks

What you will learn in AI & Machine Learning in Excel

These workbooks cover 55 complete courses (selection below):

• Machine Learning in Excel: How Models Really Work
• Neural Networks by Hand in Excel
• Recommendation Systems & Text Analytics in Excel
• AI for Business: ROI, Token Costs & Automation Savings
• Evaluating AI & Search: Ranking and LLM Evaluation Metrics
• LLM Cost & Token Economics: Pricing AI Features
• Reinforcement Learning by Hand: Bandits, Q-Learning & Value Iteration
• Computer Vision Metrics: IoU, Precision-Recall & mAP by Hand
• AI Fairness & Bias Metrics: Parity, Equal Opportunity & Calibration by Group
• Anomaly Detection Basics: Z-Scores, IQR, Seasonality & Alert Precision
• Machine Learning Forecasting: Lag Features, Train/Test Splits & Error Metrics
• Text Classification by Hand: Naive Bayes Spam Filter & Sentiment
• Decision Trees & Ensembles by Hand: Entropy, Information Gain, Random Forests & Boosting
• Embeddings & Vector Search by Hand: Cosine Similarity, Top-k Retrieval & Recall
• AI Chatbot Support Metrics: Containment, Deflection, CSAT & Cost per Resolution
• Clustering Methods by Hand: Hierarchical, k-Medoids, Silhouette & DBSCAN
• Speech Recognition Metrics: Word Error Rate, CER & Real-Time Factor
• Genetic Algorithms by Hand: Selection, Crossover, Mutation & Fitness
• GPU Inference Capacity Planning: Tokens per Second, Batching, Memory & Cost
• ML Model Monitoring: Data Drift, PSI, KS Test & Performance Decay
• Data Labeling for AI: Inter-Annotator Agreement, Label Quality & Labeling Cost
• Image Processing by Hand: Pixels, Convolution Kernels, Edges & Pooling
• Feature Engineering for Machine Learning: Encoding, Scaling, Binning & Leakage
• Fuzzy Matching & Record Linkage: Normalising Names, Similarity Scores & Duplicates
• AI Model Training & Fine-Tuning Budgets: Tokens, Compute, GPU-Hours, Experiment Grids & Scaling Curves
• AI Agents by the Numbers: Step Success Rates, Retries, Tool-Call Costs, Human Hand-Offs & Cost per Completed Task
• How Language Models Work by Hand: Tokens, Embeddings, Attention Weights, Softmax & Sampling with Temperature
• How Neural Networks Learn: Gradient Descent, Momentum, RMSProp, Adam, Learning-Rate Schedules & Warm-Up by Hand
• How Image Generators Work by Hand: Noise Schedules, Forward Noising, Denoising Steps, Guidance Scale & Latent Sizes
• Document AI in Operations: Field Extraction Accuracy, Confidence Thresholds, Straight-Through Processing, Review Queues & Cost per Document
• Explaining Model Predictions by Hand: Contributions, Shapley Values, Permutation Importance, Partial Dependence & Counterfactuals
• How Text Prediction Worked Before Transformers: n-Gram Counts, Smoothing, Backoff, Perplexity, Autocomplete & Text Generation by Hand
• Cross-Validation & Hyperparameter Tuning by Hand: Train/Validation/Test Splits, k-Fold Scores, Grid Search, Overfitting Curves & Data Leakage
• Hidden Markov Models by Hand: States & Emissions, the Forward Algorithm, Viterbi Decoding, Smoothing & Simple Learning from Counts
• The Kalman Filter by Hand: Noisy Measurements, Predict & Update Steps, Kalman Gain, Constant-Velocity Tracking, Sensor Fusion & Tuning
• How Tokenizers Work by Hand: Characters & Pairs, Byte-Pair Merges, Vocabulary Size, Tokens per Word, Sequence Length & Embedding-Table Costs
• Recurrent Networks by Hand: Hidden States, Unrolling Through Time, Vanishing Gradients, LSTM Gates, GRU Cells & Sequence Prediction
• Support Vector Machines by Hand: Separating Lines, Margins, Hinge Loss, Soft Margins, Kernel Tables & Support Vectors
• Autoencoders & GANs by Hand: Encoders, Bottlenecks, Reconstruction Error, Anomaly Scores, Latent Space, Variational Sampling, Generator vs Discriminator Losses & Mode Collapse
• Scoring Generated Text by Hand: N-Gram Overlap, Clipped Precision, BLEU & Brevity Penalty, ROUGE Recall, Token F1, Exact Match & Agreement with Human Ratings

Prices are $14.99 per PDF workbook. One-time payment, no subscription.