As the demand for data-driven insights continues to grow, machine learning has become an essential skill for any aspiring data scientist. But where do you start if you don't have a background in programming or mathematics? In this post, I'll share a detailed strategy for learning machine learning from scratch, tailored specifically for those who have a strong foundation in other areas but may not have much experience with ML. Here's a detailed strategy for someone to learn machine learning in detail for data science:
- Start with the basics: Begin by studying fundamental machine learning topics such as supervised and unsupervised learning, regression, classification, clustering, and decision trees. You can begin by taking online courses like Andrew Ng's Machine Learning course on Coursera and if you are a book lover you can start by reading the book "Python Machine Learning" by Sebastian Raschka. You can also watch videos from Youtube which were completely free. Some best source is
Krish Naik
sir channel. Even I also started learnning machine learning there and I personally recommend you guys to check his channel out here Youtube
.
- Code your own models: Once you've mastered the fundamentals, you can begin building your own models with popular libraries like scikit-learn or TensorFlow. Begin with simple models such as linear regression and logistic regression before progressing to more complicated models such as neural networks and deep learning. There are so many references out there in Internet. So, Just have a look.
- Work on projects: Working on real-world projects is the most effective approach to learn machine learning. Find a dataset that interests you and use it to create a model to address a problem. Kaggle is a fantastic tool for finding datasets and competing in machine learning competitions.
- Keep up with new developments: Machine learning is a rapidly expanding discipline, with new advancements occurring on a regular basis. Follow the newest research papers, attend conferences and meetings, and join online forums like Reddit's /r/MachineLearning or LinkedIn's Machine Learning group to stay up to speed.
- Get feedback: Seek input from others as you work on projects and code your own models. Join study groups or online forums where you may share your work with other machine learning aficionados and receive constructive feedback.
By following this strategy, someone with a strong background in programming and mathematics can gain a comprehensive understanding of machine learning for data science.
Do let me know your valuable feedback in comment section and if you have any queries, I am super exicted to help you out......????.
