I want to learn Machine Learning. What is the learning path that i can follow to learning Machine Learning along with the doing projects.
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Welcome to the community.
Just to make things clear for me. Are you asking in general manners or asking about the Deeplearning.ai Course path?
Best regards
elirod
Sir,
I am asking in general manner. How can i be successful in Machine learning path. I am currently Software Developer with B.Tech(Computer Science).
If you can provide the path in both general and in DeepLearning.AI , it will be helpfull.
Thank you
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Oh, OK.
Well, the path you should follow depends on various factors, including your background, prior knowledge, learning style, and specific interests within the field. However, here’s a general learning path you can consider, which includes both theoretical learning and practical projects:
- Mathematics and Statistics: Start by building a strong foundation in mathematics and statistics, as they form the backbone of Machine Learning algorithms. Focus on linear algebra, calculus, probability, and statistics.
- Programming: Acquire proficiency in a programming language commonly used in Machine Learning, such as Python or R. Python is a popular choice due to its extensive libraries and community support.
- Fundamental Concepts: Learn the core concepts of Machine Learning, including supervised learning, unsupervised learning, and reinforcement learning. Understand key terminologies like features, labels, training, testing, and evaluation metrics.
- Machine Learning Algorithms: Dive into various Machine Learning algorithms such as linear regression, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, and neural networks.
- Hands-On Projects: Start implementing small projects to apply what you’ve learned. Work on datasets, build models, and analyze results. This practical experience is crucial for solidifying your understanding.
- Data Preprocessing: Learn about data preprocessing techniques, including data cleaning, handling missing values, feature scaling, and feature engineering.
- Model Evaluation and Validation: Understand different methods to evaluate model performance, like cross-validation, and techniques to avoid overfitting and underfitting.
- Advanced Topics: Explore more advanced topics like deep learning, natural language processing, computer vision, and reinforcement learning, based on your interests.
- Machine Learning Libraries and Frameworks: Familiarize yourself with popular libraries like scikit-learn, TensorFlow, Keras, PyTorch, and others, which provide powerful tools for building and training models efficiently.
- Participate in Kaggle Competitions: Engage in Kaggle competitions to challenge yourself, learn from others, and work on real-world problems.
Remember, learning is an iterative process. Continuously practice and revisit concepts to reinforce your understanding. Working on diverse projects will help you gain practical experience and expose you to different challenges. Additionally, stay updated with the latest research papers and attend conferences or workshops to connect with the Machine Learning community.
With that in mind, i got some suggestions.
A good way to track your learning is follow a roadmap such as: Data Science Beginners Roadmap.
Regards the Deeplearning.ai, you can follow this path:
- Mathematics for Machine Learning and Data Science Specialization
- Machine Learning Specialization
- Deep Learning Specialization
- Machine Learning Engineering for Production (MLOps) Specialization
Please, keep in mind that this are just suggestions. Feel free to learn the path that is more suitable for you.
Your learning path have to be based on your interests and goals, as Machine Learning offers a vast and ever-evolving landscape with numerous opportunities to explore. Happy learning and best of luck on your Machine Learning journey!
I hope this help you.
Best regards
elirod
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Sir,
Thank you for your guidance.
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You welcome.
Don’t forget to mark this topic as a Solution. This takes my response to the top of discussion in order to help other learners.
best regards
elirod
Hi @LarengeKamal,
one of our mentors @saifkhanengr published an excellent outline on how a possible course sequence can look like: check out this post!
A quite classic sequence which I observed more and more among fellow learners is:
- AI for everyone (if you are a beginner) [I understand this is where you currently are - pls. correct me if this is not correct]
- machine learning specialization for the basics and core concepts
- deep learning specialization if this suits your plans and you work rather with big unstructured data and want to apply or work with CV, NLP, LLM etc.
- (MLOps, LLM specialization or TF specialization dependent on your requirements and plans)
My take: the best personal sequence for you depends on:
- where you stand now (e.g beginner or medium)
- what you want to achieve (e.g. become an AI engineer in the field of IoT / Automotive)
- your strength and background of the industry you want to work (e.g. background in image processing with focus on sensor fusion and deep learning)
- your timeline, considering how much time you want to invest in your learning roadmap (e.g. 5 hours per week for 8 months or so)
see also this thread: Please help with course selection - #2 by Christian_Simonis
Best regards
Christian
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In addition to the two excellent and helpful responses above, I would like to point out that even after you complete all the Deep Learning Specializations with 100% score, you will only be at the doorstep of being ‘good’ at Machine Learning. The reason is that these classes for the most part focus on a single ‘happy’ path through the code where everything works. They don’t train you how to adapt to new requirements or inputs, or trouble shoot when your training doesn’t converge, for example. My suggestion echos some of what is provided above, which is to take an algorithm or problem, maybe medical X-rays from the AI for Medicine course, and really take it apart and put it back together. Tinker with the architectures, change the batch sizes, adjust the learning rates, find a different data set and get the model to work well with that one, too. Read relevant journal papers and PhD dissertations. Be patient…It’s a journey.
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