ML Training & Evaluation Guidance

@glanegonsalves

Hi, Glane here. I can mentor you through the complete ML workflow in a structured, beginner-friendly way, focusing on data splitting, cross-validation, hyperparameter tuning, model selection, leakage prevention, and overfitting. We’ll use Python and scikit-learn with practical notebooks and short exercises after each session, covering accuracy, precision, recall, F1, ROC-AUC, learning curves, and how to choose the right metric. I can also provide feedback on your exercises and gradually introduce TensorFlow/PyTorch once the fundamentals are clear. We can structure this around 1-2 live sessions per week, with clear learning materials and hands-on practice throughout.

@AhmadXpert2410

Hi there, I’m a Data Scientist / AI Engineer with hands-on experience in machine learning, statistical analysis, Python, and scikit-learn. I’d be happy to mentor you through the fundamentals with a practical, structured approach focused on understanding why models work, how they are trained and validated, and how to evaluate their real performance. I can structure the sessions around data splitting, cross-validation, preprocessing pipelines, feature engineering, hyperparameter tuning, model selection, and avoiding data leakage and overfitting. We can then work through accuracy, precision, recall, F1, ROC-AUC, confusion matrices, learning curves, and model interpretation using practical Python/scikit-learn examples rather than purely theoretical explanations. After each session, I can provide a focused coding exercise or notebook and review your solution, explaining mistakes and better approaches. Live screen-sharing sessions once or twice per week can be used for Q&A, coding walkthroughs, and gradually progressing toward more advanced topics such as TensorFlow/PyTorch once the fundamentals are solid. Regards, Ahmad

@codeandcommand

Python and TensorFlow are both listed, so I'll assume that's your working stack unless you say otherwise. Rather than lecture theory, I'd have you build and evaluate a real model, starting with data prep and metrics, then move into PyTorch once the fundamentals click. Can start today, first session ready within a day or two. The budget and timeline here are rough starting points based on the post, we'll firm them up once I know your current level and target topics. Want to jump on a quick call?

@adriank06

I can structure your learning path around Python and scikit-learn, covering train/validation/test splits, cross-validation, hyperparameter tuning and model selection. The workflow will be: data preparation → experiment design → training → validation → evaluation metrics → error analysis → model selection. Do you already have a dataset or project in mind that you’d like us to use during the sessions? I’m a Python and Machine Learning developer with hands-on experience building and evaluating ML projects. I work with Python, scikit-learn, NumPy, Pandas and modern ML workflows, including classification and model evaluation. I can combine short live sessions with practical notebooks and exercises so each concept is immediately applied. I’ll also focus on avoiding data leakage, overfitting and choosing the right metrics for each problem. I can provide clear explanations, practical exercises and feedback on your solutions throughout the engagement. I can start immediately and offer the complete 7-day mentoring plan for **$25**.

@rakhshanz3

As an experienced educator with in-depth knowledge and proficiency in your desired ML topic, I am confident that my teaching methods will help you master the fundamentals of machine learning. With two decades of experience across academia and the tech industry, including a PhD in AI and roles such as CTO at an AI startup, I have a strong understanding of the practical applications and theoretical concepts involved. My teaching approach aims at building comprehensive understanding rather than simply imparting knowledge. I will provide you with a well-structured learning plan tailored to your needs using Python and libraries like scikit-learn; but can also branch into TensorFlow or PyTorch when you're ready. Regular coding exercises with subsequent feedback will facilitate your progress while live voice or video sessions will enable interactive learning. What's more, my profound comprehension of ML doesn't end at just knowing how to build models and optimize pipelines, but extends to training workflows, evaluation metrics (accuracy, precision, recall, F1), hyper-parameter tuning, model selection, data splits/rightsizing, cross-validation etc to perfectly fit the Vista of your needs. By leveraging my expertise and your commitment, we can build a strong foundation for you to interpret learning curves and make informed decisions needed for tackling real-world ML projects.

@nintysix96

Treat the bid amount and timeline as rough starting points, we'll firm them up once we know how many sessions you're thinking and over what period. What you're after makes a lot of sense. You don't want to ship something yet, you want to build the mental model first , understand why a model fails, how to catch leakage before it bites you, and how to actually read what the numbers are telling you. That's a solid foundation before touching any real project, and it's the kind of learning that sticks. Here's how we'd structure our time together: - Learning path: a session-by-session plan covering data splits and cross-validation first, then hyperparameter tuning and model selection, with scikit-learn as the main tool before branching into TensorFlow or PyTorch once the core ideas are solid. - Evaluation metrics: practical walkthroughs of accuracy, precision, recall, F1 and ROC AUC with examples of when each one is the right call and why the wrong choice can quietly mislead you. - Hands-on notebooks: short coding exercises in Jupyter after each session, with written feedback on your solutions so you can spot and fix gaps right away rather than carrying misunderstandings forward. - Live sessions: one to two video calls per week with screen sharing so you can ask questions in real time and we can walk through code together rather than you staring at an error alone. After a short chat about your current level and how many hours a week you can put in, we'll put together a written plan with session topics, materials and a firm price. Want to jump on a quick call this week so we can sketch out the first few sessions? Best, 96 Studio

@FizzaMushtaq07

As a Python and Machine Learning developer with 2 plus years of experience, I can mentor you through practical model training, validation, evaluation, and experiment design. Poor validation choices can make a model appear accurate while failing on unseen data. I would structure sessions around scikit learn notebooks, covering leakage prevention, train validation splits, cross validation, hyperparameter tuning, model selection, and metrics such as F1 and ROC AUC. The key technical trade off is choosing evaluation metrics based on class imbalance and business consequences rather than defaulting to accuracy. Would you prefer binary classification examples first, or should we use regression alongside classification? Happy to discuss details in chat. Regards, Fizza Mushtaq

@Tradeware

Hi, I can help you build a strong understanding of core machine-learning concepts through a structured, practical learning path rather than simply giving you theory. We can work through the complete workflow using Python and scikit-learn, including train/validation/test splits, cross-validation, hyperparameter tuning, model selection, data leakage, overfitting, and learning curves. I’ll also explain accuracy, precision, recall, F1, and ROC AUC with practical examples so you understand when each metric is appropriate. After each session, I can provide short coding exercises or notebook-based tasks and review your solutions, explain mistakes, and gradually increase the difficulty. Once the fundamentals are solid, we can branch into TensorFlow or PyTorch and connect the concepts to deep learning. I’d structure the sessions around practical experiments so you finish able to design reliable ML evaluations and make informed modeling decisions. Please share your current Python/ML level and preferred session frequency, and we can plan the first lesson.

@wassimay24

Hello, I would be happy to help you build a solid understanding of machine learning training and evaluation. I have hands-on experience with Python and machine-learning workflows, including data preparation, train/test splitting, cross-validation, hyperparameter tuning, model comparison, and performance evaluation. I can structure the learning progressively, starting with scikit-learn and practical examples before moving to more advanced tools when appropriate. We can work on accuracy, precision, recall, F1-score, ROC-AUC, overfitting, data leakage, learning curves, and model selection through clear explanations and short Python exercises. I can also provide Jupyter/Colab notebooks after the sessions so you can practice independently, review your solutions, and discuss any mistakes or questions during our next session. My objective is not only to show you how to run ML models, but to help you understand why each training and evaluation decision is made. I am available for live screen-sharing sessions and would be glad to discuss the learning plan with you. Best regards

@Cole608

Hi! I can walk you through ML training and evaluation from the ground up. We'll cover the core ideas in plain language, then the math behind them — loss functions, gradient descent, why your model underfits or overfits. I'll show you how to pick the right metric for your problem (accuracy vs F1 vs ROC-AUC, and when each one lies to you), and we'll run small TensorFlow examples so you can see it working. I can also review your existing code and debug it with you line by line. To make the session useful right away, tell me: what dataset or model are you working on? Then I'll tailor the examples to it. Thanks! Cole / JC Works

@justam

As a seasoned Data Scientist and advanced statistical analyst, I am confident that I can provide you with the comprehensive, hands-on training and guidance you need to achieve your goal of understanding the fundamentals of machine learning deeply. My vast experience in designing and implementing predictive models using deep learning architectures and advanced data modeling aligns perfectly with your needs. Furthermore, my adeptness in leveraging Python and libraries like scikit-learn will give you practical knowledge that can be easily built upon once the basics are firmly grasped.

@dixondambo

At $10-30 this needs to be scoped as one focused session, not open-ended mentoring, otherwise you get rushed answers on train/validation/test splits, overfitting, and metric selection without the reasoning sticking. I'd structure it around a concrete example: pick one small dataset or problem type you're curious about, walk through why a split is chosen, what metric fits the judgment call (accuracy vs precision/recall vs RMSE), and where cross-validation earns its keep versus just adding noise. Happy to do a free 15-minute call here on Freelancer first to confirm what you already know and what's actually worth covering in the paid session, no payment or commitment needed. Do you have a specific dataset or problem type in mind, or should the session use a generic example to teach the concepts? Kind regards, Dixon

@zaryabk83

Hi, I'm Zaryab. I can mentor you through ML fundamentals with a focus on training, validation, and evaluation — using Python and scikit-learn, with room to branch into TensorFlow or PyTorch once the basics are solid. How I'd structure our time: - **Session 1–2** — full training workflow: train/val/test splits, cross-validation, why leakage and overfitting happen and how to avoid them. - **Session 3–4** — evaluation metrics: accuracy, precision, recall, F1, ROC-AUC — with examples of when each is the right choice, and why accuracy often misleads. - **Session 5–6** — hyper-parameter tuning, model selection, and reading learning curves to decide what to adjust. - **Ongoing** — a short notebook exercise after each session, with written feedback on your solutions so mistakes get corrected before the next topic. Cadence: 1–2 live voice/video sessions per week, screen-share friendly. I'll share a structured learning path up front and point you to free, high-quality resources rather than paid ones. By the end, you should be able to design experiments without leakage, choose sensible metrics per problem type, and interpret learning curves confidently. Quick questions: - What's your current Python level — comfortable with pandas/numpy, or starting fresh? - Do you have a specific ML problem in mind to anchor the exercises, or would you rather work on standard datasets? - Preferred session times and timezone? Best, Zaryab

@hkhkkh

Learning ML fundamentals from a mentor works when every concept arrives with the decision it lets you make. Here is the path I would walk you through, and the order matters. 1. Week one, the workflow itself: train/validation/test split, why the test set is touched exactly once, and cross-validation. You write the split code yourself so you feel where the leakage happens. The single most common mistake at this stage is scaling or feature-selecting before the split - the model then looks great in validation and disappoints in production. I make you reproduce that failure once on purpose, because you will not repeat it after that. 2. Week two, metrics as decisions, not numbers. Accuracy, precision, recall, F1, ROC AUC, and what each one is lying about. Concretely: on a dataset that is 95% negative, predicting "negative" for everything scores 95% accuracy and is useless; that is the week accuracy stops being your default. You will compute all of them on the same model and explain to me which one you would ship and why. 3. Week three, hyper-parameter tuning: grid versus random search versus Bayesian, and why random search is usually the right first move. We also cover what a learning curve tells you - whether you need more data or a better model - because that is the question that decides most real projects. 4. Week four onwards, model selection end to end on one small dataset of your choosing, then the same workflow rebuilt in TensorFlow or PyTorch once the scikit-learn version is solid. Each session is 60 minutes with a working notebook, and you get a written summary plus two exercises. I review your code and tell you what I would change, not just whether it runs. Two things worth knowing early, because they change conclusions rather than just improve them: cross-validation must contain your preprocessing inside the loop, or your scores are optimistic by a wide margin; and ROC AUC is over-optimistic on heavily imbalanced data, which is where the precision-recall curve is the honest picture. Tell me your current level - have you trained any model before, and in what language - and I will adjust the path. First session can be this week; bring your own dataset if you have one.