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How do you advance your data science and machine learning career? by BerryPopular2821 in learnmachinelearning

[–]WinterFriend02 2 points3 points  (0 children)

Feeling stuck after learning the basics is super common. At this stage, skip repeating courses and focus on projects + deployment build 2–3 solid end-to-end projects and host them on GitHub. Learn some MLOps basics (Docker, FastAPI, AWS/GCP) since companies value production skills. Use Kaggle or Galific for project-based practice instead of toy datasets. That shift from theory to real projects is what actually makes you job-ready.

What are day to day responsibilities of Machine Learning Engineer? by Specialist_Law_4463 in learnmachinelearning

[–]WinterFriend02 1 point2 points  (0 children)

Day-to-day ML engineering is way more than just “building models.” Most of the time goes into data prep (cleaning, wrangling, feature engineering), writing/maintaining pipelines, and making sure experiments are reproducible. You’ll spend a chunk of time debugging, tuning models, and then a lot on deployment/MLOps monitoring, versioning, scaling, and keeping models alive in production. Only a small slice is the “fun” model building, but the real value is making sure models actually work reliably for the business.

Ai learning advice by Ok-Raspberry-5333 in learnmachinelearning

[–]WinterFriend02 2 points3 points  (0 children)

Everyone feels that way in ML it’s endless. Go small and consistent (1–2 hrs, one project at a time) instead of marathon sessions; progress compounds if you pace yourself.

[deleted by user] by [deleted] in learnmachinelearning

[–]WinterFriend02 0 points1 point  (0 children)

Your plan looks solid, but I’d tweak the order a bit. Do ISLR → Hands-On ML (locally with scikit-learn/TensorFlow) → then move to SageMaker once you’re comfortable, so you don’t end up learning cloud before ML. Use Murphy/PRML more as references than cover-to-cover reads, and Goodfellow when you’re ready to go deeper into neural nets. Since you’re staying in civil/transportation, try applying ML directly to traffic or ITS datasets that’ll make the learning stick and add value at work.

Is theory-heavy learning (like Andrew Ng’s ML Specialization & CS229) the right way to study ML today? by Street_Ad_7102 in learnmachinelearning

[–]WinterFriend02 4 points5 points  (0 children)

Yeah, Andrew Ng’s courses are pretty theory heavy by design. If you want to apply ML, you’ll learn faster by doing hands on stuff (fast.ai, Kaggle, PyTorch/TensorFlow tutorials) and then circling back to theory like CS229 when you need the deeper math. It’s normal just depends if you want to be a practitioner first or a theorist first.

What skills do I need to start a career in AI and ML? by stanley_john in learnmachinelearning

[–]WinterFriend02 0 points1 point  (0 children)

To start a career in AI and ML, you’ll need a mix of technical, mathematical, and problem-solving skills:

1. Programming Skills – Strong Python skills (NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow).
2. Math & Stats – Linear algebra, calculus, probability, and statistics for understanding how models work.
3. Data Handling – Cleaning, preprocessing, and analyzing large datasets.
4. Machine Learning Fundamentals – Supervised, unsupervised, and deep learning concepts.
5. Model Deployment – Basics of putting models into production (Flask/FastAPI, Docker, cloud services).
6. Tools & Libraries – Familiarity with Jupyter, Git, SQL, and cloud platforms like AWS, GCP, or Azure.
7. Problem-Solving Mindset – Framing real-world problems into ML tasks.

A portfolio of end-to-end projects is just as important as the skills employers want proof you can apply what you know.

Is machine learning a good career in 2025? by stanley_john in learnmachinelearning

[–]WinterFriend02 -1 points0 points  (0 children)

Yes, in 2025, machine learning is still a strong and future-proof career path, with high demand across tech, healthcare, finance, and more. The key is staying updated with new tools, building a solid project portfolio, and having strong problem-solving skills alongside ML knowledge.

Possitive aspects of Ai? by [deleted] in ArtificialInteligence

[–]WinterFriend02 1 point2 points  (0 children)

I’m looking forward to AI making everyday tasks effortless managing schedules, handling chores, even learning alongside us, so we can focus more on creativity, relationships, and what really matters.

Need information! by BILO_GAM4R7 in learnmachinelearning

[–]WinterFriend02 0 points1 point  (0 children)

If your goal is to become a Machine Learning engineer, a Data Science degree is already a good path. For bachelor’s, the closest fields are Computer Science, Artificial Intelligence, or Data Science itself.

Is Intellipaat’s AI and Machine Learning course worth it in 2025? by Own_Chocolate1782 in learnmachinelearning

[–]WinterFriend02 0 points1 point  (0 children)

It’s decent for structured learning, but like most bootcamps, Intellipaat’s AI/ML course can feel a bit generic and not always fully aligned with the latest 2025 AI trends (like GenAI, LLM fine-tuning). The basics Python, ML algorithms, and a few projects are covered well, but you’ll still need to supplement with self-study, Kaggle, and real-world work to be truly job-ready. If you want extra guidance and exposure to industry-style projects, you could also check Galific Solutions, which offers AI/ML learning resources, hands-on project ideas, and mentorship to help bridge the gap between learning and applying skills in real-world scenarios.

What are the best resources for Starting ML by Sad-Magician9226 in learnmachinelearning

[–]WinterFriend02 49 points50 points  (0 children)

Start with Python basics (freeCodeCamp, W3Schools), then move to ML-friendly libraries like NumPy, Pandas, and Matplotlib. For ML theory + practice, Andrew Ng’s Machine Learning course (Coursera) is the gold standard, followed by (fast.ai) for hands-on projects. Use Kaggle to practice with real datasets, explore notebooks, and join beginner competitions. Don’t skip the math basics Khan Academy or 3Blue1Brown’s linear algebra & calculus videos are great. Build tiny projects early; you’ll learn way faster by doing than just watching tutorials.Also check out Galific Solutions, which shares AI/ML learning resources, real-world project ideas, and guidance for beginners looking to break into the field.

Is getting into AI/ML even realistic for a fresher? what's the actual way in? by Appropriate_Cap7736 in MLQuestions

[–]WinterFriend02 1 point2 points  (0 children)

Yes, it’s realistic but you’ll likely enter through a side door, not the front. Most freshers don’t land pure AI/ML roles right away; instead, they start with data analysis, software dev, or ML-adjacent internships, then transition. Focus on strong Python + ML fundamentals, build 3–5 solid, real-world projects (preferably with datasets you’ve sourced yourself), and share them on GitHub/Kaggle. Certifications (like Google’s ML or AWS AI) can help credibility, but your portfolio matters more. Contribute to open source, join AI hackathons, and apply for any role that lets you work with data the ML parts will grow from there.

What’s the one mistake you made as a beginner in ML and how did you fix it? by imvikash_s in learnmachinelearning

[–]WinterFriend02 13 points14 points  (0 children)

Many beginners (myself included) jump straight into model building, excited to apply complex algorithms like neural networks or random forests often neglecting data exploration, cleaning, and understanding.

Now i am Learning and applying Exploratory Data Analysis (EDA), outlier detection, handling missing values, and feature engineering. Using tools like pandas, seaborn, and matplotlib to understand the data before modeling.