ml-model-training

Training machine learning models involves selecting appropriate algorithms, preparing data, and optimizing model parameters to achieve strong predictive performance.

aj-geddes/useful-ai-promptsUpdated Apr 8, 2026

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Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill ml-model-training

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Installation Guide

How to use ml-model-training on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add ml-model-training
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill ml-model-training

Fetches ml-model-training from aj-geddes/useful-ai-prompts and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/ml-model-training

Restart Cursor to activate ml-model-training. Access via /ml-model-training in your agent's command palette.

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

ML Model Training

Training machine learning models involves selecting appropriate algorithms, preparing data, and optimizing model parameters to achieve strong predictive performance.

Training Phases

  • Data Preparation: Cleaning, encoding, normalization
  • Feature Engineering: Creating meaningful features
  • Model Selection: Choosing appropriate algorithms
  • Hyperparameter Tuning: Optimizing model settings
  • Validation: Cross-validation and evaluation metrics
  • Deployment: Preparing models for production

Common Algorithms

  • Regression: Linear, Ridge, Lasso, Random Forest
  • Classification: Logistic, SVM, Random Forest, Gradient Boosting
  • Clustering: K-Means, DBSCAN, Hierarchical
  • Neural Networks: MLPs, CNNs, RNNs, Transformers

Python Implementation

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import (accuracy_score, precision_score, recall_score,
                            f1_score, confusion_matrix, roc_auc_score)
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
import tensorflow as tf
from tensorflow import keras

# 1. Generate synthetic dataset
np.random.seed(42)
n_samples = 1000
n_features = 20

X = np.random.randn(n_samples, n_features)
y = (X[:, 0] + X[:, 1] - X[:, 2] + np.random.randn(n_samples) * 0.5 > 0).astype(int)

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Normalize features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

print("Dataset shapes:")
print(f"Training: {X_train_scaled.shape}, Testing: {X_test_scaled.shape}")
print(f"Class distribution: {np.bincount(y_train)}")

# 2. Scikit-learn models
print("\n=== Scikit-learn Models ===")

models = {
    'Logistic Regression': LogisticRegression(max_iter=1000),
    'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42),
    'Gradient Boosting': GradientBoostingClassifier(n_estimators=100, random_state=42),
}

sklearn_results = {}
for name, model in models.items():
    model.fit(X_train_scaled, y_train)
    y_pred = model.predict(X_test_scaled)
    y_pred_proba = model.predict_proba(X_test_scaled)[:, 1]

    sklearn_results[name] = {
        'accuracy': accuracy_score(y_test, y_pred),
        'precision': precision_score(y_test, y_pred),
        'recall': recall_score(y_test, y_pred),
        'f1': f1_score(y_test, y_pred),
        'roc_auc': roc_auc_score(y_test, y_pred_proba)
    }

    print(f"\n{name}:")
    for metric, value in sklearn_results[name].items():
        print(f"  {metric}: {value:.4f}")

# 3. PyTorch neural network
print("\n=== PyTorch Model ===")

class NeuralNetPyTorch(nn.Module):
    def __init__(self, input_size):
        super().__init__()
        self.fc1 = nn.Linear(input_size, 64)
        self.fc2 = nn.Linear(64, 32)
        self.fc3 = nn.Linear(32, 1)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(0.3)

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.relu(self.fc2(x))
        x = self.dropout(x)
        x = torch.sigmoid(self.fc3(x))
        return x

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
pytorch_model = NeuralNetPyTorch(n_features).to(device)
criterion = nn.BCELoss()
optimizer = torch.optim.Adam(pytorch_model.parameters(), lr=0.001)

# Create data loaders
train_dataset = TensorDataset(torch.FloatTensor(X_train_scaled),
                             torch.FloatTensor(y_train).unsqueeze(1))
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

# Train PyTorch model
epochs = 50
pytorch_losses = []
for epoch in range(epochs)

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Steps

  1. 1Install skill using provided installation command
  2. 2Test with simple use case relevant to your work
  3. 3Evaluate output quality and relevance
  4. 4Iterate on prompts to improve results
  5. 5Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use when

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid when

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Related Skills

Reviews

4.829 reviews
  • G
    Ganesh MohaneDec 12, 2024

    Keeps context tight: ml-model-training is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • R
    Rahul SantraNov 3, 2024

    Registry listing for ml-model-training matched our evaluation — installs cleanly and behaves as described in the markdown.

  • P
    Pratham WareOct 22, 2024

    ml-model-training reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • R
    Ren NasserSep 13, 2024

    Keeps context tight: ml-model-training is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • R
    Ren BansalSep 9, 2024

    ml-model-training has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • S
    Sakura AbebeAug 28, 2024

    Solid pick for teams standardizing on skills: ml-model-training is focused, and the summary matches what you get after install.

  • L
    Layla AbebeAug 4, 2024

    ml-model-training is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • O
    Omar FloresJul 23, 2024

    ml-model-training reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • I
    Isabella ThomasJul 19, 2024

    We added ml-model-training from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • S
    Sakshi PatilJul 15, 2024

    I recommend ml-model-training for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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