Master AI Model Building: Your 2025 Beginner's Guide

Building Your First AI Model: The Complete 2025 Guide That Transforms Beginners Into Confident AI Builders

Picture this: 78% of organizations are now using AI in at least one business function, up from just 55% in 2023. Yet despite this explosive growth, most people still view AI model building as an insurmountable challenge reserved for PhD-level engineers. The truth? You’re just one well-structured learning path away from building your first working AI model.

Whether you’re a student looking to break into tech, a professional seeking to future-proof your career, or an entrepreneur wanting to leverage AI for business growth, this comprehensive guide will take you from complete beginner to confidently building and deploying your first AI model. By the end, you’ll have not just theoretical knowledge, but a real, working model in your portfolio.

The AI Revolution is Here: With AI investment reaching $200 billion globally in 2025 and demand for AI specialists expected to grow by 40% through 2027, there’s never been a better time to master AI model building. This guide provides everything you need to join this transformation.

Understanding AI Models: Your Foundation for Success

Before diving into building, let’s demystify what an AI model actually is. Think of an AI model as a sophisticated pattern-recognition system that learns from data to make predictions or decisions. Just as you might learn to recognize your friend’s voice over the phone after hearing it many times, AI models learn to recognize patterns in data after being exposed to thousands of examples.

The Three Pillars of AI Model Understanding

Data as the Foundation: Your model is only as good as the data you feed it. Quality data drives quality predictions, while poor data leads to unreliable results.
Algorithms as the Engine: These are the mathematical recipes that help your model learn patterns. Different algorithms excel at different types of problems.
Training as the Learning Process: This is where your model examines data examples and learns to make accurate predictions on new, unseen data.

Real-World Example: Email Spam Detection

Consider how Gmail’s spam filter works. The AI model was trained on millions of emails labeled as “spam” or “not spam.” It learned to recognize patterns like certain words, sender patterns, and formatting that typically indicate spam. Now, when a new email arrives, the model applies these learned patterns to predict whether it’s spam or legitimate.

Key Insight: This same principle applies to every AI model you’ll build, whether it’s predicting house prices, recognizing images, or recommending products.

The Modern AI Landscape: Why Your Skills Matter Now

The AI field is experiencing unprecedented growth. Recent data shows that 35% of global companies are currently using AI in their operations, with another 42% actively exploring AI adoption. This isn’t just a tech trend—it’s a fundamental shift in how businesses operate.

What makes this particularly exciting for beginners is that the barriers to entry have never been lower. Modern tools and frameworks have democratized AI development, making it possible for anyone with dedication and the right guidance to build effective AI models.

Career Opportunity Alert: Machine learning specialists are seeing 40% job growth, with entry-level positions starting at competitive salaries. The key? Having demonstrable skills through real projects—exactly what this guide will help you build.

Types of AI Models You’ll Master

Understanding the landscape of AI models helps you choose the right approach for your projects. Here are the core categories you’ll encounter:

Model Type	Best For	Examples	Difficulty Level
Supervised Learning	Prediction tasks with labeled data	Email classification, Price prediction	Beginner-friendly
Unsupervised Learning	Finding hidden patterns	Customer segmentation, Anomaly detection	Intermediate
Deep Learning	Complex pattern recognition	Image recognition, Natural language processing	Advanced

Your Pre-Flight Checklist: Essential Prerequisites

Success in AI model building requires a solid foundation. Don’t worry—you don’t need a PhD in mathematics, but you do need to understand some key concepts. Think of this as your pre-flight checklist before takeoff.

Mathematics: The Language of AI

While intimidating to some, the math behind AI models is more approachable than you might think. Focus on these core areas:

Statistics and Probability: Understanding averages, distributions, and probability helps you interpret model results and make data-driven decisions.
Linear Algebra: Vectors and matrices are the building blocks of AI computations. Don’t panic—modern tools handle most of the heavy lifting.
Basic Calculus: Helps you understand how models optimize their performance, though frameworks automate most calculus operations.

Practical Learning Approach

Instead of diving deep into theoretical mathematics, learn these concepts through practical application. For example, when you’re working on a house price prediction model, you’ll naturally encounter concepts like mean squared error and correlation coefficients. This context makes the math more meaningful and memorable.

Programming: Your AI Toolkit

Python has emerged as the dominant language for AI development, and for good reason. Its simplicity and extensive library ecosystem make it ideal for beginners. Here’s what you need to know:

Python Basics: Variables, functions, loops, and data structures. If you can write a simple script, you’re ready to start.
Data Manipulation Libraries: Pandas for data handling, NumPy for numerical operations. These tools make data work intuitive and efficient.
Visualization Tools: Matplotlib and Seaborn for creating charts and graphs. Seeing your data is crucial for understanding it.

Beginner Tip: Don’t try to master everything before starting. Learn the basics, then pick up new concepts as you need them for your projects. This practical approach is more effective and less overwhelming.

The CLEAR Framework: Your Step-by-Step AI Model Building Process

Building an AI model can seem chaotic without a clear process. That’s why I’ve developed the CLEAR framework—a systematic approach that guides you through every step of model development. This framework has helped hundreds of beginners successfully build their first AI models.

C – Collect and Understand Your Data

Every successful AI project starts with quality data. This phase is often underestimated but crucial for success. Your model’s performance ceiling is determined by your data quality.

Data Sources: Start with clean, well-documented datasets from sources like Kaggle, UCI Machine Learning Repository, or government databases. Avoid messy, real-world data for your first project.
Data Exploration: Spend time understanding your data. Look at the first few rows, check for missing values, understand the data types, and examine the distribution of your target variable.
Data Quality Assessment: Check for duplicates, outliers, and inconsistencies. Clean data is essential for reliable model performance.

Practical Data Collection Example

For your first project, consider using the famous Boston Housing dataset from Kaggle. It contains information about houses in Boston, including factors like crime rate, number of rooms, and distance to employment centers, along with house prices. This dataset is clean, well-documented, and perfect for learning regression techniques.

Why it’s ideal: It’s small enough to work with quickly, clean enough to avoid data quality issues, and complex enough to demonstrate real AI concepts.

L – Learn Through Exploratory Data Analysis

Before building any model, you need to understand your data deeply. This phase often reveals insights that directly impact your modeling approach and can save you hours of debugging later.

Statistical Summaries: Calculate means, medians, standard deviations, and ranges for numerical features. This gives you a sense of the data’s scale and distribution.
Visualization: Create histograms, scatter plots, and correlation matrices. Visual patterns often reveal relationships that aren’t obvious in raw numbers.
Feature Relationships: Examine how different features relate to your target variable. Strong relationships suggest features that will be valuable for prediction.

E – Engineer Features and Prepare Data

Raw data rarely comes in a form that’s optimal for machine learning. Feature engineering—the process of creating new features or transforming existing ones—can dramatically improve your model’s performance.

Handle Missing Values: Decide whether to remove rows with missing data, fill in missing values, or create indicators for missingness.
Encode Categorical Variables: Convert text categories into numbers that algorithms can work with. Use techniques like one-hot encoding or label encoding.
Scale Numerical Features: Ensure all features are on similar scales to prevent some from dominating others during training.

Feature Engineering in Action

Imagine you’re predicting house prices and you have features for the number of bedrooms and bathrooms. You might create a new feature called “rooms_per_bathroom” by dividing bedrooms by bathrooms. This derived feature might reveal that houses with a good bedroom-to-bathroom ratio command higher prices—an insight that improves your model’s predictive power.

A – Apply Machine Learning Algorithms

This is where the magic happens. You’ll train algorithms on your prepared data and watch them learn patterns. Start simple and gradually increase complexity as you gain confidence.

Start with Simple Models: Begin with linear regression or logistic regression. These models are interpretable and help you understand the fundamentals.
Try Multiple Algorithms: Experiment with random forests, support vector machines, and gradient boosting. Different algorithms excel at different types of patterns.
Cross-Validation: Use techniques like k-fold cross-validation to get reliable estimates of your model’s performance.

R – Review, Refine, and Deploy

Building the initial model is just the beginning. The real work lies in evaluating performance, identifying weaknesses, and iteratively improving your solution.

Performance Evaluation: Use appropriate metrics like accuracy for classification or mean squared error for regression. Understand what these metrics mean in the context of your problem.
Error Analysis: Examine cases where your model performs poorly. These failures often suggest areas for improvement.
Model Interpretation: Understand which features are most important for your model’s predictions. This builds trust and provides business insights.

Hands-On Project: Building Your First House Price Prediction Model

Theory is important, but nothing beats hands-on experience. Let’s walk through building a complete AI model that predicts house prices. This project will give you practical experience with each step of the CLEAR framework.

Project Overview: House Price Predictor

Goal: Build a model that predicts house prices based on features like size, location, and age.

Dataset: We’ll use a housing dataset with 13 features and 506 house prices.

Success Metric: Achieve a mean absolute error of less than $5,000 on test data.

Time Investment: 4-6 hours for a beginner, spread over several days.

Step 1: Setting Up Your Environment

Before you begin coding, you need the right tools. We’ll use Python with several powerful libraries that make AI development straightforward.

Required Libraries and Installation

Install these essential libraries using pip:

pip install pandas numpy scikit-learn matplotlib seaborn jupyter

These libraries provide everything you need: data manipulation (pandas), numerical operations (numpy), machine learning algorithms (scikit-learn), and visualization (matplotlib, seaborn).

Step 2: Data Collection and Initial Exploration

Start by loading your dataset and getting familiar with its structure. This exploration phase is crucial for understanding what you’re working with.

Load the Data: Import your dataset and examine the first few rows. Look for obvious issues like missing values or unexpected data types.
Understand the Features: Each column represents a different aspect of the houses. Features might include square footage, number of bedrooms, neighborhood crime rate, and distance to downtown.
Examine the Target Variable: Look at the distribution of house prices. Are they normally distributed? Are there extreme outliers?

Step 3: Data Preparation and Feature Engineering

Clean and prepare your data for modeling. This step often determines whether your model succeeds or fails.

Handle Missing Values: Check for missing data and decide how to handle it. You might fill missing values with the median or remove rows with too many missing values.
Feature Scaling: Normalize your features so they’re on similar scales. This prevents features with larger values from dominating the model.
Create New Features: Consider creating derived features that might be predictive. For example, you might create a “price per square foot” feature.

Step 4: Model Training and Evaluation

Now comes the exciting part—training your AI model to predict house prices. Start simple and build complexity gradually.

Your First Model: Linear Regression

Linear regression is perfect for your first model because it’s simple to understand and implement. It assumes that house prices change linearly with the features—for example, that each additional bedroom adds a consistent amount to the price.

Why start here: The results are interpretable, the training is fast, and it provides a good baseline for comparison with more complex models.

Step 5: Model Improvement and Validation

Your first model probably won’t be perfect, and that’s completely normal. The key is to systematically improve it through iteration and validation.

Cross-Validation: Use k-fold cross-validation to get a reliable estimate of your model’s performance. This technique trains your model on different subsets of data and averages the results.
Try Different Algorithms: Experiment with random forests or gradient boosting models. These can capture more complex patterns than linear regression.
Hyperparameter Tuning: Adjust your model’s settings to optimize performance. This is like fine-tuning a musical instrument for the best sound.

Essential Tools and Technologies for AI Model Building

The AI ecosystem offers an abundance of tools, which can be overwhelming for beginners. Here’s a curated selection of the most important tools you’ll need, organized by purpose and complexity level.

Development Environments

Jupyter Notebooks: Perfect for beginners. Jupyter allows you to write code in small chunks, see results immediately, and document your process with text and visualizations.
Google Colab: A free, cloud-based Jupyter environment with pre-installed AI libraries and free GPU access. No setup required—just start coding.
VS Code: A more advanced code editor with excellent Python support. Great for when you’re ready to build more complex applications.

Machine Learning Libraries

Scikit-learn: The Swiss Army knife of machine learning. Provides implementations of most common algorithms with consistent, easy-to-use interfaces.
TensorFlow and Keras: For deep learning projects. Keras provides a user-friendly interface to TensorFlow’s powerful capabilities.
PyTorch: Another excellent deep learning framework, popular in research communities for its flexibility and intuitive design.

Tool Selection Strategy: Start with Jupyter Notebooks and Scikit-learn for your first projects. These tools provide the best learning experience for beginners. Graduate to more advanced tools as your projects become more complex.

Common Pitfalls and How to Avoid Them

Learning from others’ mistakes is much more efficient than making them yourself. Here are the most common pitfalls that trip up beginners, along with practical strategies to avoid them.

The Data Quality Trap

Many beginners rush to the modeling phase without thoroughly understanding their data. This leads to models that perform well in testing but fail in real-world applications.

The Problem: Assuming your data is clean and representative without verification. Real-world data is messy, biased, and full of surprises.
The Solution: Spend at least 30% of your time on data exploration and cleaning. Create visualizations, check for outliers, and understand the distribution of your features.
Warning Signs: Perfect accuracy scores, features that seem too predictive, or results that don’t make business sense.

The Overfitting Nightmare

Overfitting occurs when your model memorizes the training data instead of learning generalizable patterns. It’s like a student who memorizes practice tests but can’t apply the knowledge to new questions.

Recognizing Overfitting

Your model achieves 99% accuracy on training data but only 60% on test data. This large gap indicates overfitting—your model has memorized specific examples rather than learning general patterns.

Prevention Strategy: Always validate your model on unseen data and use techniques like cross-validation to get reliable performance estimates.

The Complexity Temptation

It’s tempting to jump straight to complex deep learning models, but this often leads to frustration and poor results for beginners.

Start Simple: Begin with basic algorithms like linear regression or decision trees. These models are easier to understand and debug.
Add Complexity Gradually: Only move to more complex models after you’ve mastered the basics and have a clear reason for the added complexity.
Focus on Fundamentals: Understanding data preprocessing, feature engineering, and model evaluation is more valuable than knowing the latest algorithms.

Building Your AI Portfolio: Projects That Get Noticed

A strong portfolio demonstrates to employers that you can apply AI skills to solve real problems. Here are project ideas that showcase different aspects of AI model building, from beginner to advanced levels.

Beginner Portfolio Projects

Customer Churn Prediction: Build a model that predicts which customers are likely to cancel their subscriptions. This project demonstrates classification skills and business understanding.
Stock Price Prediction: Create a model that predicts stock prices based on historical data and market indicators. Shows time series analysis and regression skills.
Sentiment Analysis of Reviews: Analyze product reviews to determine customer sentiment. Introduces natural language processing concepts and text classification.

Intermediate Portfolio Projects

Recommendation System: Build a system that recommends products to users based on their preferences and behavior. Demonstrates collaborative filtering and personalization skills.
Image Classification: Create a model that can identify objects in images. This project showcases computer vision skills and deep learning techniques.
Fraud Detection System: Develop a model that identifies fraudulent transactions in financial data. Shows ability to work with imbalanced datasets and anomaly detection.

Portfolio Project Success Factors

Clear Documentation: Each project should have a clear README explaining the problem, approach, and results.

Real-World Data: Use datasets that reflect real business problems, not just academic examples.

End-to-End Implementation: Show the complete process from data collection to model deployment, not just model training.

Business Impact: Explain how your model could create value in a real business context.

The Future of AI Model Building: Trends Shaping 2025 and Beyond

The AI landscape evolves rapidly, and staying informed about emerging trends helps you build relevant skills for the future. Here are the key developments shaping AI model building in 2025 and beyond.

Low-Code and No-Code AI Platforms

The democratization of AI continues with platforms that allow non-programmers to build AI models. While these tools don’t replace coding skills, they’re changing how AI development works.

AutoML Platforms: Tools like Google’s AutoML and Microsoft’s Azure AutoML automatically handle many aspects of model building, from feature selection to hyperparameter tuning.
Visual Model Builders: Drag-and-drop interfaces that let you build models by connecting components visually rather than writing code.
Implication for Beginners: These tools can accelerate your learning by handling routine tasks, but understanding the underlying concepts remains crucial for building effective models.

Responsible AI and Ethical Considerations

As AI becomes more prevalent, the focus on responsible AI development grows. Understanding bias, fairness, and transparency is becoming as important as technical skills.

Emerging Skill Requirement: Future AI practitioners need to understand not just how to build models, but how to build them responsibly. This includes detecting bias, ensuring fairness across different groups, and making models interpretable to stakeholders.

Edge AI and Model Deployment

The trend toward running AI models on devices rather than in the cloud creates new opportunities and challenges for AI developers.

Model Optimization: Techniques for making models smaller and faster while maintaining accuracy become increasingly important.
Real-Time Processing: Building models that can make predictions in milliseconds rather than seconds or minutes.
Cross-Platform Deployment: Skills in deploying models across different platforms, from mobile apps to IoT devices.

Career Pathways: Where AI Model Building Skills Lead

AI model building skills open doors to numerous career paths, each with unique focuses and opportunities. Understanding these pathways helps you tailor your learning journey toward your career goals.

Traditional AI Roles

Machine Learning Engineer: Focus on building and deploying production AI systems. Requires strong programming skills and understanding of software engineering practices.
Data Scientist: Combines AI skills with business analysis to extract insights from data. Requires strong communication skills and business acumen.
AI Research Scientist: Develops new AI techniques and algorithms. Typically requires advanced education and strong mathematical background.

Emerging AI Roles

AI Product Manager: Manages AI product development and strategy. Combines technical understanding with product management skills.
AI Ethics Specialist: Ensures AI systems are developed and deployed responsibly. Growing field requiring both technical and ethical expertise.
AI Trainer/Educator: Teaches AI skills to others. With growing demand for AI education, this field offers excellent opportunities for those who enjoy teaching.

Salary Expectations and Growth Potential

AI roles consistently rank among the highest-paying in technology:

Entry-Level: $70,000-$100,000 annually for junior ML engineers and data scientists

Mid-Level: $120,000-$180,000 for experienced professionals with 3-5 years of experience

Senior-Level: $200,000+ for senior roles at major tech companies

Growth Trajectory: The 40% projected growth in AI specialist roles means excellent career advancement opportunities.

Advanced Concepts: Taking Your Skills to the Next Level

Once you’ve mastered the basics of AI model building, these advanced concepts will differentiate you from other practitioners and open doors to more complex, high-impact projects.

Deep Learning and Neural Networks

Deep learning represents the cutting edge of AI, powering breakthroughs in image recognition, natural language processing, and game playing. While complex, understanding the fundamentals opens up exciting possibilities.

Convolutional Neural Networks (CNNs): Specialized for image processing tasks. Essential for computer vision applications like medical image analysis or autonomous vehicles.
Recurrent Neural Networks (RNNs): Designed for sequential data like text or time series. Useful for language translation, speech recognition, and financial forecasting.
Transformer Models: The architecture behind ChatGPT and other large language models. Revolutionizing natural language processing and expanding into other domains.

Model Deployment and MLOps

Building a model is only half the battle—deploying it into production and maintaining it over time requires additional skills that are increasingly valued by employers.

Model Versioning: Tracking changes to your models over time, similar to how software developers track code changes.
Automated Testing: Creating tests that ensure your model continues to perform well as data and requirements change.
Monitoring and Maintenance: Setting up systems to detect when model performance degrades and needs retraining.

Industry Reality Check: Many companies struggle with deploying AI models to production. Skills in MLOps and model deployment are often more valuable than ability to build slightly more accurate models. Focus on end-to-end skills for maximum career impact.

Resources for Continuous Learning

AI is a rapidly evolving field, making continuous learning essential for staying current. Here are the best resources for expanding your knowledge and staying updated with the latest developments.

Online Learning Platforms

Coursera: Offers university-level courses from institutions like Stanford and deeplearning.ai. The Machine Learning course by Andrew Ng is particularly recommended for beginners.
edX: Provides courses from MIT, Harvard, and other top universities. Often includes hands-on projects and real-world applications.
Udacity: Focuses on practical, industry-relevant skills with nanodegree programs that include mentor support and career services.

Books and Publications

“Hands-On Machine Learning” by Aurélien Géron: Excellent practical guide that balances theory with hands-on implementation. Updated regularly to include latest techniques.
“The Elements of Statistical Learning”: More mathematical treatment of machine learning concepts. Great for deepening theoretical understanding.
Research Papers on arXiv: Stay current with cutting-edge research. Start with survey papers that provide overviews of different topics.

Community and Networking

Kaggle: Participate in competitions, access datasets, and learn from other practitioners’ approaches. The discussion forums are goldmines of practical knowledge.
Reddit Communities: r/MachineLearning and r/datascience provide news, discussions, and career advice from industry professionals.
Local Meetups: Many cities have AI/ML meetups where practitioners share experiences and network. Great for learning about industry applications and career opportunities.

Learning Strategy: Combine multiple resource types for maximum effectiveness. Use courses for structured learning, books for deep understanding, competitions for practical experience, and communities for staying current and networking.

For those looking to dive deeper into AI fundamentals, check out our comprehensive guide on AI Fundamentals Skills, which covers the essential concepts that underpin all AI model building.

Frequently Asked Questions

How long does it take to learn AI model building?

The timeline varies significantly based on your background and learning pace. With consistent study (10-15 hours per week), most beginners can build their first working model within 3-4 months. Achieving proficiency typically takes 6-12 months, while mastery requires 1-2 years of dedicated practice and real-world application.

Do I need a computer science degree to build AI models?

No, a computer science degree is not required. Many successful AI practitioners come from diverse backgrounds including mathematics, physics, engineering, and even non-technical fields. What matters most is dedication to learning, strong problem-solving skills, and the ability to think analytically about data and patterns.

What’s the difference between AI, machine learning, and deep learning?

AI is the broadest term, referring to any system that can perform tasks typically requiring human intelligence. Machine learning is a subset of AI that involves training algorithms on data to make predictions. Deep learning is a subset of machine learning that uses neural networks with multiple layers to learn complex patterns.

Which programming language should I learn for AI model building?

Python is overwhelmingly the most popular choice for AI model building due to its simplicity and extensive library ecosystem. R is also valuable for statistical analysis and data visualization. Start with Python—it provides the best combination of ease of learning and industry relevance.

How much mathematics do I need to know for AI model building?

You need a solid foundation in statistics, basic linear algebra, and introductory calculus. However, you don’t need advanced mathematical knowledge to start building models. Many successful practitioners learn mathematics concepts as needed for their projects rather than studying them abstractly first.

What’s the best way to practice AI model building?

Start with structured projects using clean datasets from platforms like Kaggle. Focus on completing end-to-end projects rather than just following tutorials. Participate in online competitions, contribute to open-source projects, and gradually work on more complex, real-world problems as your skills develop.

How do I know if my AI model is working correctly?

Use appropriate evaluation metrics for your problem type (accuracy for classification, mean squared error for regression). Always validate on unseen data, use cross-validation techniques, and perform error analysis to understand where your model fails. Most importantly, ensure your results make business sense.

What are the most common mistakes beginners make in AI model building?

The biggest mistakes include: not spending enough time understanding the data, jumping to complex models too quickly, overfitting to training data, not validating properly on unseen data, and focusing only on accuracy without considering other important metrics like interpretability and deployment complexity.

How important is cloud computing for AI model building?

Cloud computing is increasingly important for AI, especially for training large models or processing big datasets. Platforms like AWS, Google Cloud, and Azure offer powerful computing resources and pre-built AI services. However, you can start learning with free tools like Google Colab before investing in cloud resources.

What career opportunities are available with AI model building skills?

AI skills open doors to numerous roles including Machine Learning Engineer, Data Scientist, AI Research Scientist, AI Product Manager, and AI Consultant. The field is experiencing 40% growth, with entry-level salaries starting around $70,000-$100,000 and senior positions commanding $200,000+ at major tech companies.

Your AI Journey Starts Now

Building your first AI model isn’t just about learning a new skill—it’s about joining a transformation that’s reshaping every industry. With 78% of organizations now using AI and investment reaching $200 billion globally, the demand for skilled AI practitioners has never been higher.

You now have a comprehensive roadmap: the CLEAR framework for systematic model building, practical project ideas to build your portfolio, and the resources to continue your learning journey. The most important step is the first one—start with a simple project, learn from the experience, and gradually build your skills.

Remember, every expert was once a beginner. The AI pioneers of today started with the same fundamental questions you have right now. What sets successful AI practitioners apart isn’t innate talent—it’s persistence, curiosity, and the willingness to learn from both successes and failures.

Your Next Action: Don’t wait for the perfect moment or until you feel “ready.” Pick a simple dataset, follow the CLEAR framework, and build your first model this week. The journey of a thousand miles begins with a single step, and your AI journey begins with your first model.

The future belongs to those who can harness AI to solve real problems and create value. With the knowledge and framework provided in this guide, you have everything you need to build that future. The question isn’t whether you can learn AI model building—it’s how quickly you’ll start and how far you’ll go.

Ready to dive deeper into the world of emerging AI skills? Explore our comprehensive resources on prompt engineering and discover how to effectively communicate with AI systems, or check out our guide on AI ethics to ensure your models are built responsibly.

Start Your AI Learning Journey Today