Hey there! I totally get it – starting out in machine learning and deep learning can be overwhelming. You know the basics of C and Python, which is a great foundation. Now, you’re looking to dive into numpy and pandas, and wondering what’s the correct roadmap to follow.
First, let’s break it down into smaller chunks. For machine learning, you’ll want to learn about supervised and unsupervised learning, regression, classification, and clustering. Deep learning is a subset of machine learning, so you’ll want to explore neural networks, convolutional neural networks, and recurrent neural networks.
Here’s a suggested roadmap to get you started:
1. **Math and Statistics**: Brush up on linear algebra, calculus, probability, and statistics. Khan Academy and Coursera are great resources for this.
2. **Python and Libraries**: Learn numpy, pandas, and scikit-learn. Practice implementing machine learning algorithms using these libraries.
3. **Machine Learning Fundamentals**: Study supervised and unsupervised learning, regression, classification, and clustering. Andrew Ng’s Machine Learning course on Coursera is a great resource.
4. **Deep Learning**: Learn about neural networks, convolutional neural networks, and recurrent neural networks. TensorFlow and Keras are popular deep learning frameworks.
5. **Practice and Projects**: Apply your knowledge by working on projects and practicing with datasets from Kaggle or UCI Machine Learning Repository.
6. **Stay Up-to-Date**: Follow machine learning and deep learning blogs, research papers, and attend conferences or meetups to stay current.
Remember, learning machine learning and deep learning takes time and practice. Stay motivated, and don’t be afraid to ask for help when you need it.
What do you think? Is there a particular aspect of machine learning or deep learning you’re most interested in?