Roadmap for AI and ML for Non Mathematical Background

Hi everyone, we are back from the summer holidays!

Today, we’re diving into an exciting topic: the roadmap to learning AI and Machine Learning (ML) for those without a mathematical background. I understand this might raise some eyebrows—Machine Learning without math? But it's true. In today's world, having an intuitive grasp of how Machine Learning works is becoming essential for everyone, regardless of their background. So, let's go on this journey together and simplify AI and ML in a way that's accessible and engaging for all.

1. Understanding AI and ML Fundamentals

• Introduction to AI and ML: Basic concepts, history, and real-world applications.
• Key Terminologies: AI, ML, Deep Learning, Neural Networks, etc.
• Types of ML: Supervised, Unsupervised, and Reinforcement Learning.

2. Building a Foundation in Data Science

• Basic Data Concepts: Data types, data structures, and databases.
• Data Collection and Cleaning: Sources of data, data preprocessing, handling missing values.
• Data Visualization: Tools like Matplotlib, Seaborn, and Tableau for visualizing data.

3. Learning Programming Skills

• Python for AI/ML: Basics of Python programming, libraries like NumPy, Pandas, Scikit-learn.

4. Diving into Machine Learning

• Basic ML Algorithms: Linear Regression, Logistic Regression, Decision Trees, K-Nearest Neighbors.
• Model Evaluation: Metrics like accuracy, precision, recall, F1-score.
• Feature Engineering: Techniques for selecting and transforming variables for better model performance.

5. Exploring Advanced Topics

• Deep Learning: Introduction to neural networks, TensorFlow, and Keras.
• Natural Language Processing: Basics of NLP, text preprocessing, sentiment analysis.
• Computer Vision: Image processing, Convolutional Neural Networks (CNNs).

6. Practical Applications and Projects

• Hands-on Projects: Reimplementing ML models on datasets like Titanic survival, handwritten digit recognition.
• Competitions and Challenges: Participating in Kaggle competitions to apply learned skills.
• Building a Portfolio: Showcasing projects and skills on GitHub or personal websites.

7. Staying Updated

• Online Courses and Tutorials: Platforms like us, Coursera, edX, Udacity for continuous learning.
• Books and Research Papers: Read latest research papers and materials.
• Community and Networking: Joining AI/ML communities, attending conferences, and networking with professionals in the field.

This roadmap provides a clear path for individuals from non-mathematical backgrounds to learn and excel in AI and ML.

Thank you

See you soon 😄