Machine Learning Math Tutor based on book mathematics for machine learning

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@intelliresponse

Hello, I can help you with structured tutoring in mathematics for machine learning, covering linear algebra, calculus, probability, and their practical applications. I can explain concepts clearly, connect theory with ML algorithms like gradient descent, PCA, and regression, and provide Python/MATLAB examples with guided exercises. My approach would include interactive lessons, practice problems after each topic, solution walkthroughs, and short recap notes so you can track your progress. I focus on building strong fundamentals while making the concepts practical and easier to apply in real machine learning projects.

@dvcontact

Hello Sir, How would you like a tailored hands-on learning experience that bridges math concepts with practical machine learning applications? My approach emphasizes interactive lessons, custom practice sets, and concise recap notes, ensuring you gain a strong grasp of the core ideas in mathematics for machine learning. Let's connect to discuss how I can help you achieve your math learning goals. Best, Smith

@shrec

Hi, I’d structure the lessons around the Mathematics for Machine Learning book, keeping each topic practical and hands-on rather than purely theoretical. A typical session would start with a short concept explanation, followed by a worked ML example, then guided practice where you solve problems step by step. For relevant topics, we can use Python or MATLAB to connect the mathematics directly to applications such as gradient descent, PCA, and logistic regression. After each session, I’d provide a focused practice set with solution walkthroughs and brief recap notes. I can adapt the pace to your beginner level and focus on building the specific skills you listed: matrix factorizations, hand-calculated gradients, and basic probability. For tools, I’d suggest Zoom/Google Meet plus Python or MATLAB for interactive exercises. Best, Ivane

@emilesd617

Hi, I am a python and machine learning developer with 8 years of rich experience in software development, with a strong background in applied mathematics, data science, and machine learning. I am familiar with Python, MATLAB, NumPy, linear algebra, calculus, probability, statistics, gradient descent, PCA, logistic regression, data visualization, and machine learning algorithms. For your lessons, I can follow *Mathematics for Machine Learning* step by step, combining each topic with worked examples, Python or MATLAB exercises, guided problem-solving, practice sets, and short recap notes focused on practical ML applications. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Thanks. Emile.

@divyam12345

Hello Dear, I understand your requirements and can provide simple, hands-on lessons covering mathematics for machine learning. I can teach vectors, matrices, calculus, probability, and distributions using worked examples, Python/MATLAB code, and guided practice. I will structure each session with explanation, practical examples, exercises, solution walkthroughs, and short recap notes. I look forward to working with you. Let’s connect in the chatbox for further discussions. Thank You. Dr. Divya.

@sepehrhn

I am a mathematician and right now I am working as software developer and machine learning specialist in a company. check my linkdin pleas

@dataspro

Nice to meet you ,The requirements of your project match my areas of work and skills, to introduce myself. My name is Anthony Muñoz and i am the lead engineer for DS Pro IT agency. I have worked for over 10 years as a Full-Stack and software development engineer and have successfully done multiple jobs. It will be a pleasure to work together to make your project. Feel free to discuss about the project with me, greetings.

@Shahabijan

Yes — here’s a compact bid that is **well over 100 characters and under 1,500 characters**: Hi, I can help you master the mathematics behind machine learning using the *Mathematics for Machine Learning* book, with a strong focus on practical, hands-on learning. Each session will follow: **Concept → Intuition → Worked Example → Python/MATLAB Implementation → Guided Practice**. We’ll cover linear algebra, matrix factorizations, eigenvalues/eigenvectors, multivariable calculus, derivatives, gradients, integrals, probability, random variables and distributions. I’ll connect each topic directly to practical ML applications such as **PCA, gradient descent, and logistic regression**. After every session, I’ll provide customized exercises, detailed solution walkthroughs, and concise recap notes so you can review independently. I have experience with **MATLAB, Python, mathematical modelling, and ML concepts**, and I’m comfortable teaching beginners from fundamentals while gradually building toward real ML algorithms. For lessons, I can use **Zoom/Google Meet, a digital whiteboard, and Jupyter/Google Colab or MATLAB** for live coding and demonstrations. My goal is to ensure that by the end, you can confidently work with matrix factorizations, calculate basic gradients by hand, and solve fundamental probability problems independently. I’d be happy to begin with a quick assessment and create a structured learning plan based on your current level.

@binayatri

Hi, I can provide hands-on, beginner-friendly math sessions focused specifically on machine learning rather than teaching the theory in isolation. I have 13+ years of experience in software engineering, AI/ML, Python, computer vision and data analytics, so I can connect the mathematics directly to algorithms and real implementation. I’d structure each session as: concept explanation → worked ML example → Python/MATLAB implementation → guided exercises → review. We can progressively cover vectors and matrices, matrix factorizations, derivatives and gradients, multivariable calculus, probability/distributions, then apply them to PCA, gradient descent and logistic regression. After every session, I’ll provide a custom practice set with step-by-step solutions and concise recap notes. We’ll also regularly check your ability to solve problems independently against the acceptance goals. I’m comfortable using Python/Jupyter for interactive demonstrations and can use MATLAB when preferred. My experience is primarily technical/ML-focused rather than formal classroom teaching, which lets me keep the sessions practical and implementation-oriented. Let's connect and get started soon. Best regards, Binaya T.

@amirali0301

Hello, I can build this scraping/automation workflow for you and return the data in the exact structure you need. I’ve worked with Python, Selenium, Playwright, APIs, pagination, dynamic websites, data cleaning and large-scale extraction, so I can handle both the scraping and the processing side of the project. For your project, I can: Scrape the required data from your target sources Handle pagination, filters and dynamic content Clean and structure the extracted data Remove duplicates / invalid records Deliver the final data in CSV/Excel/JSON or your required format Build the workflow so it can be reused or automated if needed I can also scrape a small sample first so you can check the data quality before proceeding. If you send me the website/source + fields you need + expected output, I can review it and tell you exactly how I would approach it. I’m ready to start.

@tyronet2

You need the mathematics taught as something you can calculate, code, and apply—not a sequence of abstract lectures. My profile is new, but I bring 9+ years in software and applied AI, including Python, CNN image processing, and production AI workflows at Marin Software. I’d structure each lesson in five parts: visual intuition, a worked calculation by hand, a short Python/NumPy implementation, guided practice, and a recap with targeted exercises. We’ll progress from vectors, matrices, systems, eigenvalues, and decompositions into multivariable derivatives, gradients, integrals, random variables, conditional probability, and distributions. Each block will connect directly to an ML use case: matrix decomposition with PCA, gradients with linear/logistic regression and gradient descent, and probability with classification and model uncertainty. Practice sets will include full solutions, common mistakes, and a short readiness check before moving forward. Would you prefer all coding in Python, or should selected exercises also be demonstrated in MATLAB?

@rakhshanz3

With my educational background in Computer Science and my PhD in Artificial Intelligence, I not only possess the technical expertise but also have practical experience to be your ideal tutor. Throughout my career, including lecturing and leading AI-driven solutions, I have honed strong communication skills and a knack for breaking down complex topics into digestible concepts. This aligns with your learning style as I understand your need for hands-on activities (even in virtual sessions) paired with connecting theory to meaningful use-cases. Being well-versed in the MML book that serves as the foundation for this project, I am equipped to offer detailed sessions on linear algebra, calculus, and probability - elucidating their relevance to machine-learning algorithms like gradient descent, PCA, and logistic regression. Furthermore, my proficiency in Python and MATLAB will ensure seamless explanations incorporating relevant code snippets.

@Maxxionn

Hi , You need an expert in Python, Educational Research, Statistical Analysis, Mathematics, Tutoring, Data Science, Machine Learning (ML), Calculus, Matlab and Mathematica and MATLAB, and I have a tailor-made solution ready for you. Your project brief instantly reminded me of a recent client who faced similar challenges, and I know exactly how to execute this flawlessly for your specific needs. To ensure we hit the ground running, I have three quick questions: Are there any additional technical details or constraints not mentioned in the brief? What is the primary hurdle currently blocking your progress on this? What is your strict timeline for completion? Why trust me with your project? The Record: 250+ Projects. 6+ Years. 100+ consecutive 5-star reviews. The Standard: Zero misses. I don’t just finish the job; I guarantee flawless execution. The Availability: Full-time freelancer, online 9 AM - 9 PM EST. My biggest "heavy-hitter" projects are kept off my public portfolio to protect client confidentiality. Click 'CHAT', and I’ll immediately send over relevant, private samples so you can see the standard of my work firsthand. Best regards, Muhammad Arsalan

@novatech2210

Hey, Just wrapped a similar hands‑on ML math course for a data‑science beginner – we covered vectors, matrices, calculus, and probability with real‑world gradient‑descent, PCA, and logistic‑regression examples, all in Python and MATLAB, plus custom practice sets and recap notes. I’m the perfect fit: I design live, interactive lessons that pair theory with code, guided problem‑solving, and clear takeaway notes. My strengths are building engaging, kinesthetic sessions, creating custom problem sets, and delivering step‑by‑step walkthroughs that help students master matrix factorizations, gradient calculations, and probability basics without external help. Multiple 5‑star reviews on teaching ML math. Happy to jump on a quick call or offer a free consultation to map out your lesson structure. Chris | Lead Developer | Novatech

@haseebs74

Hi, I’d be happy to help you build a strong foundation in math for machine learning using the MML Book as our main reference. My lesson structure would be: • 10–15 min: concept explanation with intuition • 20–25 min: worked examples and visual explanations • 20–30 min: hands-on Python/MATLAB exercises • Guided problem-solving with immediate feedback • End-of-session recap + custom practice set We can cover vectors, matrices, factorizations, multivariable calculus, derivatives, integrals, probability and distributions, while continuously connecting them to practical ML concepts such as PCA, gradient descent and logistic regression. I’ll focus on understanding rather than memorization, including calculating gradients and matrix operations by hand before implementing them in code. Preferred tools: Python/Jupyter Notebook, NumPy and Matplotlib, with MATLAB when useful. We can use screen sharing/Zoom or another platform you prefer. I’ll also provide concise recap notes, detailed solution walkthroughs and track your progress toward the stated acceptance criteria. I’m ready to begin with an assessment of your current level and build the lessons around it.

@SystemX2

Hello, I am Mohammed From Tampa, FL. I understand the importance of connecting theoretical math concepts to practical machine learning applications. With over 8 years of experience in tutoring math for machine learning, I focus on making complex ideas clear and actionable. I will structure our sessions around hands-on activities, using Python or MATLAB for live coding examples to illustrate vectors, matrices, and calculus concepts. Each lesson will tie theory to algorithms such as gradient descent and PCA. After each session, you'll receive custom practice sets with detailed solutions to reinforce your learning, along with recap notes summarizing key takeaways. I’ve successfully guided beginners to confidently manipulate matrix factorizations, compute gradients, and evaluate basic probability expressions in previous roles. What specific machine learning use cases are you most interested in applying these math concepts to? Thank you.

@andrem939

Hello, I am an experienced full stack software engineer with a strong foundation in mathematics and statistics, particularly as they relate to machine learning. I can provide live, interactive lessons focusing on linear algebra, calculus, and probability, using the specified textbook as a guide. My teaching approach includes hands-on activities, detailed code snippets in Python, and guided problem-solving sessions to ensure you grasp each concept thoroughly. I have a proven track record of delivering custom practice sets and recap notes to reinforce learning. Thanks!

@nicolen272

Your goal is clear — you want to actually understand the math behind ML, not just memorize formulas, and the MML book is one of the best frameworks for doing that with real depth. Here's how I'd approach our sessions: - Each lesson starts with the concept from the MML book, then immediately connects it to a real algorithm — so when we cover eigendecomposition, you're also seeing exactly why PCA works the way it does. - Every session includes live Python or MATLAB walkthroughs where you run the code, tweak parameters, and see the math behave in real time — that kinesthetic loop is where things actually click. - After each session you get a custom problem set and a one-page recap with key takeaways and a preview of what's next, so you're never starting cold. - My proposed arc: Weeks 1–3 on linear algebra (vectors, matrices, factorizations), Weeks 4–6 on calculus (derivatives, gradients, chain rule into backprop), Weeks 7–9 on probability (distributions, Bayes, into logistic regression). A couple of quick questions before we start: Do you have a Python environment set up already, or would you prefer we use MATLAB throughout? And are there any specific ML algorithms you're most eager to understand — that can help me prioritize early wins for you. Ready to map out Session 1 whenever you are — just send me a message and we'll get started.