Welcome to the 100 Days in AI journey! This roadmap will guide you through a comprehensive learning path, from the basics to advanced concepts in Artificial Intelligence.
This roadmap is designed to help you gain a solid understanding of AI over the course of 100 days. Each day will include specific tasks, resources, and exercises to ensure a structured learning experience.
Before you begin, make sure you have the following:
- Basic programming knowledge (preferably in Python)
- Understanding of high school level mathematics
- A computer with internet access
- Willingness to learn and experiment
- Day 1: Introduction to AI
- Read about the history and applications of AI
- AI Overview
- Write a short essay on the current state and future of AI
- Day 2: AI in the Real World
- Explore different AI applications in various industries
- Watch AI For Everyone by Andrew Ng
- Day 3: Python Basics - Part 1
- Learn Python syntax and basic programming concepts
- Python for Beginners
- Complete basic exercises on HackerRank Python
- Day 4: Python Basics - Part 2
- Continue learning Python basics: loops, functions, and data structures
- Write small programs to solidify your understanding
- Day 5: Python Basics - Part 3
- Practice with Python modules and libraries
- Complete more exercises on HackerRank Python
- Day 6: Introduction to NumPy
- Learn NumPy for numerical computing
- Follow NumPy Quickstart Tutorial
- Implement basic operations with NumPy arrays
- Day 7: Introduction to Pandas
- Learn Pandas for data manipulation
- Follow Pandas Documentation
- Practice data manipulation with Pandas
- Day 8: Introduction to Matplotlib
- Learn Matplotlib for data visualization
- Follow Matplotlib Pyplot Tutorial
- Create basic plots and visualizations
- Day 9: Data Analysis with Python
- Combine NumPy, Pandas, and Matplotlib for data analysis
- Work on a mini-project using a dataset from Kaggle
- Day 10: Review and Practice
- Review Python basics, NumPy, Pandas, and Matplotlib
- Complete exercises and mini-projects to reinforce learning
- Day 11: Introduction to Linear Algebra
- Learn about vectors and vector operations
- Khan Academy Linear Algebra
- Day 12: Matrix Operations
- Study matrix multiplication, determinants, and inverses
- Practice problems on MIT OpenCourseWare
- Day 13: Eigenvalues and Eigenvectors
- Understand eigenvalues and eigenvectors
- Watch 3Blue1Brown's Essence of Linear Algebra series
- Day 14: Applications of Linear Algebra
- Explore applications of linear algebra in AI
- Implement linear algebra concepts in Python
- Day 15: Review and Practice
- Review linear algebra concepts
- Solve practice problems and implement in Python
- Day 16: Introduction to Calculus
- Learn about derivatives and their applications
- Khan Academy Calculus
- Day 17: Integrals and Their Applications
- Study integrals and their applications
- Practice problems on Brilliant.org
- Day 18: Introduction to Probability
- Learn basic probability concepts
- Khan Academy Probability
- Day 19: Probability Distributions
- Study different probability distributions
- Apply probability concepts in Python
- Day 20: Review and Practice
- Review calculus and probability concepts
- Solve practice problems and implement in Python
- Day 21: Introduction to Machine Learning
- Learn about supervised and unsupervised learning
- Watch introductory videos on Sebastian Raschka
- Day 22: Linear Regression
- Understand linear regression and its applications
- Andrew Ng's ML Course
- Implement linear regression in Python
- Day 23: Logistic Regression
- Study logistic regression for classification problems
- Implement logistic regression in Python
- Day 24: Decision Trees
- Learn about decision trees and their applications
- Implement decision trees in Python
- Day 25: Model Evaluation
- Understand model evaluation metrics
- Scikit-Learn Documentation
- Evaluate machine learning models in Python
- Day 26: Introduction to Scikit-Learn
- Learn about Scikit-Learn and its features
- Follow Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
- Day 27: Building ML Models with Scikit-Learn
- Build and train machine learning models using Scikit-Learn
- Complete exercises and projects
- Day 28: Hyperparameter Tuning
- Learn about hyperparameter tuning techniques
- Implement hyperparameter tuning in Python
- Day 29: Working with Real-World Data
- Explore and preprocess real-world datasets
- Participate in a Kaggle competition
- Day 30: Review and Practice
- Review machine learning concepts and Scikit-Learn
- Complete exercises and projects to reinforce learning
- Day 31: Introduction to Neural Networks
- Learn about perceptrons and neural network architecture
- Deep Learning Book
- Day 32: Activation Functions
- Study different activation functions and their applications
- Implement activation functions in Python
- Day 33: Forward and Backpropagation
- Understand forward and backpropagation algorithms
- Implement forward and backpropagation in Python
- Day 34: Training Neural Networks
- Learn about training neural networks and optimization techniques
- Implement training algorithms in Python
- Day 35: Neural Network Architectures
- Explore different neural network architectures
- Watch Deep Learning Specialization by Andrew Ng
- Day 36: Introduction to TensorFlow
- Learn about TensorFlow and its features
- TensorFlow Documentation or PyTorch
- Day 37: Building Models with TensorFlow
- Build and train neural network models using TensorFlow
- Complete tutorials and exercises
- Day 38: Introduction to Keras
- Learn about Keras and its features
- Keras Documentation
- Day 39: Building Models with Keras
- Build and train neural network models using Keras
- Complete tutorials and exercises
- Day 40: Model Evaluation and Tuning
- Evaluate and tune deep learning models
- Implement evaluation and tuning techniques in Python
- Day 41: Introduction to Convolutional Neural Networks (CNNs)
- Learn about CNN architecture and applications
- CS231n: CNNs for Visual Recognition
- Day 42: Building CNNs with TensorFlow
- Implement CNNs for image classification using TensorFlow
- Complete tutorials and exercises
- Day 43: Building CNNs with Keras
- Implement CNNs for image classification using Keras
- Complete tutorials and exercises
- Day 44: Transfer Learning
- Learn about transfer learning and its applications
- Implement transfer learning in Python
- Day 45: Introduction to Recurrent Neural Networks (RNNs)
- Study RNN architecture and applications
- CS224n: NLP with Deep Learning
- Day 46: Building RNNs with TensorFlow
- Implement RNNs for sequence modeling using TensorFlow
- Complete tutorials and exercises
- Day 47: Building RNNs with Keras
- Implement RNNs for sequence modeling using Keras
- Complete tutorials and exercises
- Day 48: Long Short-Term Memory (LSTM) Networks
- Learn about LSTM networks and their applications
- Implement LSTM networks in Python
- Day 49: Introduction to Generative Models
- Study Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)
- GAN Tutorial
- Day 50: Building GANs and VAEs
- Implement GANs and VAEs using TensorFlow and Keras
- Complete tutorials and exercises
- Day 51: Advanced CNN Architectures
- Learn about advanced CNN architectures (e.g., ResNet, Inception)
- Implement advanced CNNs in Python
- Day 52: Object Detection and Segmentation
- Study object detection and segmentation techniques
- Implement object detection and segmentation in Python
- Day 53: Advanced RNN Architectures
- Learn about advanced RNN architectures (e.g., GRU, BiLSTM)
- Implement advanced RNNs in Python
- Day 54: Sequence-to-Sequence Models
- Study sequence-to-sequence models and their applications
- Implement sequence-to-sequence models in Python
- Day 55: Attention Mechanisms
- Learn about attention mechanisms and their applications
- Implement attention mechanisms in Python
- Day 56: Introduction to NLP with Deep Learning
- Explore NLP applications with deep learning
- Build NLP models using TensorFlow and Keras
- Day 57: Text Classification
- Implement text classification models in Python
- Complete tutorials and exercises
- Day 58: Named Entity Recognition (NER)
- Learn about NER and its applications
- Implement NER models in Python
- Day 59: Sentiment Analysis
- Study sentiment analysis techniques
- Implement sentiment analysis models in Python
- Day 60: Transformers and BERT
- Learn about transformers and BERT
- Implement transformers and BERT models in Python
- Day 61: Introduction to Reinforcement Learning (RL)
- Study RL basics and algorithms
- Spinning Up in Deep RL
- Day 62: Q-Learning
- Learn about Q-learning and its applications
- Implement Q-learning in Python
- Day 63: Deep Q-Networks (DQN)
- Study DQN and its applications
- Implement DQN in Python
- Day 64: Policy Gradients
- Learn about policy gradients and their applications
- Implement policy gradients in Python
- Day 65: Advanced RL Algorithms
- Study advanced RL algorithms (e.g., A3C, PPO)
- Implement advanced RL algorithms in Python
- Day 66: AI Ethics and Safety
- Explore ethical considerations and bias in AI
- Watch videos and read articles on AI ethics
- Write an essay on ethical implications of AI
- Day 67: Bias and Fairness in AI
- Learn about bias and fairness in AI
- Implement techniques to mitigate bias in AI models
- Day 68: AI Safety Measures
- Study AI safety measures and practices
- Implement safety measures in AI models
- Day 69: AI and Society
- Explore the impact of AI on society
- Participate in discussions and write a report on AI and society
- Day 70: Review and Practice
- Review advanced deep learning and RL concepts
- Complete exercises and projects to reinforce learning
- Day 71: AI in Healthcare
- Explore AI applications in healthcare
- Analyze case studies and implement healthcare AI models
- Day 72: AI in Finance
- Study AI applications in finance
- Implement finance-related AI models
- Day 73: AI in Autonomous Systems
- Learn about AI applications in autonomous systems
- Analyze case studies and implement autonomous AI models
- Day 74: AI in Natural Language Processing (NLP)
- Explore advanced NLP applications
- Build advanced NLP models using TensorFlow and Keras
- Day 75: AI in Computer Vision
- Study advanced computer vision applications
- Implement advanced computer vision models in Python
- Day 76: AI in Robotics
- Explore AI applications in robotics
- Analyze case studies and implement robotics AI models
- Day 77: AI in Game Development
- Study AI applications in game development
- Implement AI models for game development
- Day 78: AI in Recommendation Systems
- Learn about recommendation systems and their applications
- Implement recommendation systems in Python
- Day 79: AI in Speech Recognition
- Study AI applications in speech recognition
- Implement speech recognition models in Python
- Day 80: AI in Anomaly Detection
- Explore AI applications in anomaly detection
- Implement anomaly detection models in Python
- Day 81: AI in Practice - Part 1
- Analyze real-world AI case studies
- Identify key takeaways and best practices
- Day 82: AI in Practice - Part 2
- Explore AI applications in different industries
- Write a report on AI applications in your industry of interest
- Day 83: AI Project Planning
- Choose an AI project and define project goals
- Gather data and plan project milestones
- Day 84: Data Collection and Preprocessing
- Collect and preprocess data for your AI project
- Implement data preprocessing techniques in Python
- Day 85: Feature Engineering
- Learn about feature engineering and its importance
- Implement feature engineering techniques in Python
- Day 86: Model Selection
- Select appropriate models for your AI project
- Implement model selection techniques in Python
- Day 87: Model Training and Evaluation
- Train and evaluate models for your AI project
- Implement training and evaluation techniques in Python
- Day 88: Model Optimization
- Optimize models for your AI project
- Implement optimization techniques in Python
- Day 89: Model Deployment
- Deploy models for your AI project
- Implement deployment techniques in Python
- Day 90: Project Review and Presentation
- Review and finalize your AI project
- Prepare a presentation and project report
- Day 91: Project Planning
- Choose a capstone project and define project goals
- Plan project milestones and deliverables
- Day 92: Data Collection and Preprocessing
- Collect and preprocess data for your capstone project
- Implement data preprocessing techniques in Python
- Day 93: Feature Engineering
- Perform feature engineering for your capstone project
- Implement feature engineering techniques in Python
- Day 94: Model Selection and Training
- Select and train models for your capstone project
- Implement training techniques in Python
- Day 95: Model Evaluation and Optimization
- Evaluate and optimize models for your capstone project
- Implement evaluation and optimization techniques in Python
- Day 96: Model Deployment
- Deploy models for your capstone project
- Implement deployment techniques in Python
- Day 97: Project Review
- Review and finalize your capstone project
- Prepare a detailed project report
- Day 98: Project Presentation Preparation
- Prepare a presentation for your capstone project
- Create presentation slides and visualizations
- Day 99: Project Presentation
- Present your capstone project to an audience
- Gather feedback and refine your presentation
- Day 100: Reflection and Future Planning
- Reflect on your 100-day AI journey
- Plan your future learning and projects in AI
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Books:
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Online Courses:
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Websites:
- Join AI communities: Participate in forums like AI Stack Exchange and Reddit's r/MachineLearning
- Practice regularly: Engage in challenges on platforms like Kaggle and HackerRank
- Stay updated: Follow AI news and research papers on ArXiv and AI conferences
By following this roadmap, you'll gain a strong foundation in AI and be prepared to tackle advanced topics and real-world projects. Stay dedicated, practice consistently, and enjoy the learning journey!
Happy Learning!
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