https://github.com/bhavik-jikadara/ai-ml-roadmap
Welcome to the ultimate guide for starting your journey in Artificial Intelligence and Machine Learning in 2025! This roadmap provides a step-by-step approach to mastering AI and ML, from fundamentals to advanced topics.
https://github.com/bhavik-jikadara/ai-ml-roadmap
artificial-intelligence computer-vision deep-learning deployment fundamentals-of-programming keras libraries machine-learning mathematics mlops natural-language-processing production-code pytorch reinforcement-learning roadmap scikit-learn tensorflow tools
Last synced: 30 days ago
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Welcome to the ultimate guide for starting your journey in Artificial Intelligence and Machine Learning in 2025! This roadmap provides a step-by-step approach to mastering AI and ML, from fundamentals to advanced topics.
- Host: GitHub
- URL: https://github.com/bhavik-jikadara/ai-ml-roadmap
- Owner: Bhavik-Jikadara
- License: apache-2.0
- Created: 2024-07-21T05:18:33.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-02-10T12:29:18.000Z (4 months ago)
- Last Synced: 2025-04-19T08:10:42.817Z (about 1 month ago)
- Topics: artificial-intelligence, computer-vision, deep-learning, deployment, fundamentals-of-programming, keras, libraries, machine-learning, mathematics, mlops, natural-language-processing, production-code, pytorch, reinforcement-learning, roadmap, scikit-learn, tensorflow, tools
- Language: JavaScript
- Homepage: https://ai-ml-roadmap.vercel.app/
- Size: 191 KB
- Stars: 50
- Watchers: 3
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# AI/ML Roadmap for Beginners in 2024
Welcome to the ultimate AI/ML roadmap for 2024! This guide is designed to help you navigate the complex world of artificial intelligence and machine learning, offering a step-by-step approach to mastering these technologies.
## 1. Fundamentals of Programming
Start with learning the basics of programming. Familiarize yourself with languages such as Python, which is widely used in AI/ML. Key topics include:
- Variables and Data Types
- Control Structures (if-else, loops)
- Functions and Modules
- Object-Oriented Programming (OOP)
- Basic Data Structures (lists, dictionaries, sets)
## 2. Mathematics for AI/ML
Mathematics forms the foundation of AI/ML. Focus on the following areas:
- Linear Algebra (vectors, matrices, eigenvalues)
- Calculus (differentiation, integration)
- Probability and Statistics (distributions, hypothesis testing)
- Optimization Techniques
## 3. Basics of AI/ML
Understand the core concepts and terminologies in AI/ML:
- What is AI? What is ML?
- Supervised vs. Unsupervised Learning
- Key algorithms: Linear Regression, Decision Trees, K-Nearest Neighbors
- Overfitting and Underfitting
- Evaluation Metrics (accuracy, precision, recall, F1-score)
## 4. Data Skills for AI/ML
Learn how to work with data, the backbone of AI/ML:
- Data Collection and Cleaning
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Data Visualization (using libraries like Matplotlib, Seaborn)
## 5. Machine Learning
Dive deeper into machine learning:
- Advanced Algorithms: SVM, Random Forests, Gradient Boosting
- Ensemble Learning
- Model Evaluation and Validation
- Hyperparameter Tuning
- Introduction to ML Frameworks (Scikit-learn, TensorFlow, PyTorch)
## 6. Deep Learning
Explore the world of deep learning:
- Neural Networks and Backpropagation
- Deep Learning Architectures (CNNs, RNNs)
- Training Deep Networks
- Transfer Learning
- Frameworks: TensorFlow, Keras, PyTorch
## 7. Natural Language Processing
Specialize in processing and analyzing text data:
- Text Preprocessing
- Sentiment Analysis
- Named Entity Recognition (NER)
- Language Models (BERT, GPT)
- Chatbots and Conversational AI
## 8. Computer Vision
Focus on techniques for processing and understanding images:
- Image Preprocessing
- Convolutional Neural Networks (CNNs)
- Object Detection and Segmentation
- Image Generation (GANs)
- Applications in Healthcare, Automotive, etc.
## 9. Reinforcement Learning
Learn about agents and environments:
- Markov Decision Processes (MDP)
- Q-Learning and Deep Q-Networks (DQN)
- Policy Gradient Methods
- Applications in Game AI, Robotics
## 10. Tools and Libraries
Familiarize yourself with essential tools and libraries:
- Jupyter Notebooks
- Scikit-learn
- TensorFlow and Keras
- PyTorch
- Pandas and Numpy
## 11. Build AI/ML Applications
Apply your knowledge to build real-world applications:
- End-to-end Machine Learning Projects
- Deployment of Models (using Flask, Docker)
- Model Monitoring and Maintenance
- Case Studies and Examples
## 12. Knowledge on Recent Trends and Advancements
Stay updated with the latest in AI/ML:
- Read Research Papers
- Follow AI/ML Blogs and News
- Participate in Competitions (Kaggle, DrivenData)
- Join AI/ML Communities and Meetups
## 13. The Super Duper NLP Repo
Check out the "Super Duper NLP Repo" for a comprehensive collection of NLP resources and projects.
## Follow
Connect with me on various platforms:
- [LinkedIn](https://www.linkedin.com/in/bhavikjikadara)
- [GitHub](https://github.com/Bhavik-Jikadara)
- [Facebook](https://www.facebook.com/Bhavikjikadara07)
- [Instagram](https://www.instagram.com/bhavikjikadara/)
- [Twitter](https://twitter.com/BhavikJikadara1)
## Subscribe
Stay tuned for more content by subscribing to my YouTube channel: [YouTube](https://www.youtube.com/channel/UC7Bp_sYQmAryrrPqvUp6PwQ)
## Donate & Support Us
If you find this guide helpful, consider supporting us through donations: [PayPal](https://www.paypal.com/paypalme/bhavikjikadara)
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### Feel free to explore each section, and don't hesitate to reach out if you have any questions or need further guidance. Happy learning