I want to learn machine learning for that what are the things i need to learn from scratch

Learning machine learning from scratch requires a structured approach. Here's a step-by-step guide on the essential topics and skills you need to learn:

1. Python Programming: Start by learning Python, as it's one of the most popular programming languages for machine learning. Familiarize yourself with Python syntax, data structures, functions, and libraries.

2. Mathematics: Machine learning involves various mathematical concepts. Focus on learning linear algebra (matrices, vectors), calculus (differentiation, integration), and probability/statistics (probability distributions, hypothesis testing).

3. Data Manipulation and Analysis: Learn how to work with data using libraries like NumPy and Pandas. You should be comfortable with data cleaning, filtering, aggregation, and visualization.

4. Machine Learning Fundamentals: Understand the basic principles of machine learning, including supervised and unsupervised learning, classification, regression, clustering, and overfitting/underfitting.

5. Machine Learning Libraries: Familiarize yourself with popular machine learning libraries like Scikit-learn, TensorFlow, and Keras. These libraries provide pre-built algorithms and tools to simplify your machine learning projects.

6. Model Evaluation and Validation: Learn about different evaluation metrics for machine learning models and techniques like cross-validation to assess model performance.

7. Feature Engineering: Understand the importance of feature engineering, where you extract meaningful features from your data to improve model performance.

8. Supervised Learning Algorithms: Dive deeper into supervised learning algorithms like linear regression, logistic regression, decision trees, random forests, support vector machines, and k-nearest neighbors.

9. Unsupervised Learning Algorithms: Study unsupervised learning algorithms such as k-means clustering, hierarchical clustering, and dimensionality reduction techniques like principal component analysis (PCA).

10. Deep Learning: Explore the basics of neural networks and deep learning. Understand concepts like activation functions, backpropagation, and different architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

11. Natural Language Processing (NLP): If you're interested in NLP, learn about text processing, sentiment analysis, and language models like Word2Vec and LSTM.

12. Model Deployment: Learn how to deploy machine learning models into production, using platforms like Flask, Django, or cloud-based solutions like AWS or Azure.

13. Practical Projects: Work on real-world machine learning projects. Apply what you've learned to solve problems, and learn from the challenges you encounter.

14. Keep Up with the Field: Machine learning is a rapidly evolving field. Stay updated by reading research papers, following blogs, and participating in online communities like GitHub and Stack Overflow.

Remember that learning machine learning is an iterative process. Start with the basics and gradually move to more complex topics as you gain confidence. Practice is key, so work on projects and challenges to solidify your understanding. Be patient with yourself, as mastering machine learning takes time and dedication. Good luck on your learning journey!