Answers.Body: <p>I graduated with a university degree in Artificial Intelligence, I only covered Artificial Intelligence a little bit on it, surprisingly, but this brings me to the actual point of my answer. I have started a bit here and there to try and learn myself I mainly use the site <a href="https://datacamp.com" rel="nofollow noreferrer">https://datacamp.com</a> a lot if I am honest, as for Python when I learned this I tried to stick to one resource and learn algorithms and problems later. This is a good resource on Python: <a href="https://roadmap.sh/python" rel="nofollow noreferrer">https://roadmap.sh/python</a> and also this course is good which is free: <a href="https://futurecoder.io/" rel="nofollow noreferrer">https://futurecoder.io/</a></p>
  <p>I mean your list is good, but what I would say try and find overlaps more in what you are learning because especially I find myself with programming say doing a Python programming course at beginner level, then doing another at beginner level but 'data science' you will find a lot of overlap.</p>
  
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Answers.CreationDate: 2023-04-20T23:21:03.263Z
Answers.LastActivityDate: 2023-04-20T23:21:03.263Z
Answers.UserDisplayName: jamiecropley
Body: <p>People, I am a chemistry undergrad that wants to learn ML for application in my field(chemistry), however, I had never studied programming or anything related to code in my life, for this reason, I am building a study plan for this goal.</p>
  <p>Can you give me any advice on this endeavor? I would appreciate any suggestions to know if I am on the correct path.</p>
  <p><strong>Cycle 1: Learning the basic concepts of Programming</strong></p>
  <p>• Algorithm and Programming Logic with Python: <a href="https://www.youtube.com/playlist?list=PLQKlKz1J9WWTUUMnfa2PWaPU3qPND7GJN" rel="nofollow noreferrer">https://www.youtube.com/playlist?list=PLQKlKz1J9WWTUUMnfa2PWaPU3qPND7GJN</a></p>
  <p>• Kaggle: <a href="https://www.kaggle.com/learn" rel="nofollow noreferrer">https://www.kaggle.com/learn</a></p>
  <p><strong>Cycle 2: Learning how to Program in Python</strong></p>
  <p>• Python Course for Beginners: <a href="https://www.youtube.com/playlist?list=PLyqOvdQmGdTSEPnO0DKgHlkXb8x3cyglD" rel="nofollow noreferrer">https://www.youtube.com/playlist?list=PLyqOvdQmGdTSEPnO0DKgHlkXb8x3cyglD</a></p>
  <p>• Kaggle: <a href="https://www.kaggle.com/learn" rel="nofollow noreferrer">https://www.kaggle.com/learn</a></p>
  <p>• CodingBat: <a href="https://codingbat.com/python" rel="nofollow noreferrer">https://codingbat.com/python</a></p>
  <p>• Project Euler: <a href="https://projecteuler.net/" rel="nofollow noreferrer">https://projecteuler.net/</a></p>
  <p><strong>Cycle 3: Learning how to use Python for Data Analysis and learning Statistics</strong></p>
  <p>• Python Course for Machine Learning and Data Analysis: <a href="https://www.youtube.com/playlist?list=PLyqOvdQmGdTR46HUxDA6Ymv4DGsIjvTQ-" rel="nofollow noreferrer">https://www.youtube.com/playlist?list=PLyqOvdQmGdTR46HUxDA6Ymv4DGsIjvTQ-</a></p>
  <p>• Khan Academy: <a href="https://pt.khanacademy.org/math/em-mat-estatistica" rel="nofollow noreferrer">https://pt.khanacademy.org/math/em-mat-estatistica</a></p>
  <p>• Pandas, Matplotlib and NumPy:</p>
  <p><a href="https://www.youtube.com/playlist?list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS" rel="nofollow noreferrer">https://www.youtube.com/playlist?list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS</a></p>
  <p><a href="https://www.youtube.com/playlist?list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB" rel="nofollow noreferrer">https://www.youtube.com/playlist?list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB</a>_</p>
  <p><a href="https://www.youtube.com/playlist?list=PLzgPDYo_3xukqLLjNeuCxj4CwvkJin03Z" rel="nofollow noreferrer">https://www.youtube.com/playlist?list=PLzgPDYo_3xukqLLjNeuCxj4CwvkJin03Z</a></p>
  <p>• How to go from ZERO to Data Science in Just One Lesson: <a href="https://www.youtube.com/watch?v=WJE4spsP-Xk" rel="nofollow noreferrer">https://www.youtube.com/watch?v=WJE4spsP-Xk</a></p>
  <p><strong>Cycle 4: Learning basic concepts about Machine Learning and Probability</strong></p>
  <p>• Course Introduction to Machine Learning and Machine Learning Algorithms:</p>
  <p><a href="https://www.youtube.com/playlist?list=PLyqOvdQmGdTSqkutrKDaVJlEv-ui1MyK4" rel="nofollow noreferrer">https://www.youtube.com/playlist?list=PLyqOvdQmGdTSqkutrKDaVJlEv-ui1MyK4</a></p>
  <p><a href="https://www.youtube.com/playlist?list=PLyqOvdQmGdTS4PcZsAKhXgkdUyLjXnptS" rel="nofollow noreferrer">https://www.youtube.com/playlist?list=PLyqOvdQmGdTS4PcZsAKhXgkdUyLjXnptS</a></p>
  <p>• Machine Learning with Python and Scikit-learn: <a href="https://www.youtube.com/playlist?list=PLeFetwYAi-F9Z0N3ZSjPL2i24PO5iiXaY" rel="nofollow noreferrer">https://www.youtube.com/playlist?list=PLeFetwYAi-F9Z0N3ZSjPL2i24PO5iiXaY</a></p>
  <p>• Kaggle: <a href="https://www.kaggle.com/learn" rel="nofollow noreferrer">https://www.kaggle.com/learn</a></p>
  <p>• Khan Academy: <a href="https://pt.khanacademy.org/math/em-mat-probabilidade" rel="nofollow noreferrer">https://pt.khanacademy.org/math/em-mat-probabilidade</a></p>
  <p>• Google Machine Learning Crash Course: <a href="https://developers.google.com/machine-learning/crash-course" rel="nofollow noreferrer">https://developers.google.com/machine-learning/crash-course</a></p>
  <p><strong>Cycle 5: Learning Applications of Machine Learning in Chemistry</strong></p>
  <p>• EMMSB LNCC: <a href="https://www.youtube.com/playlist?list=PLIJhet1J-_l82wpFzn2ZOyCIKy-FotuND" rel="nofollow noreferrer">https://www.youtube.com/playlist?list=PLIJhet1J-_l82wpFzn2ZOyCIKy-FotuND</a></p>
  <p>• The Computational Toolkit: <a href="https://www.youtube.com/playlist?list=PLgCwaJhZsSlMQky78U5JLyMOgO16o6ubE" rel="nofollow noreferrer">https://www.youtube.com/playlist?list=PLgCwaJhZsSlMQky78U5JLyMOgO16o6ubE</a></p>
  <p>• Learn how to use RDKit or DeepChem, a collection of cheminformatics and machine learning tools for Python</p>
  <p><strong>Cicle 6: Learning Deep Learning Fundamentals</strong></p>
  <p>•   Deep Learning by Andrew Ng on Coursera: <a href="https://www.coursera.org/specializations/deep-learning" rel="nofollow noreferrer">https://www.coursera.org/specializations/deep-learning</a></p>
  <p>•   Kaggle:
  <a href="https://www.kaggle.com/learn" rel="nofollow noreferrer">https://www.kaggle.com/learn</a></p>
  
Tags: <machine-learning><python>
Title: How a begginer should start studies in ML?
UserDisplayName: Everson Gomes
ThreadText: # How a begginer should start studies in ML?
  
  (Asked by: username_0 on 2023-04-20)
  
  People, I am a chemistry undergrad that wants to learn ML for application in my field(chemistry), however, I had never studied programming or anything related to code in my life, for this reason, I am building a study plan for this goal.
  
  Can you give me any advice on this endeavor? I would appreciate any suggestions to know if I am on the correct path.
  
  **Cycle 1: Learning the basic concepts of Programming**
  
  • Algorithm and Programming Logic with Python: <https://www.youtube.com/playlist?list=PLQKlKz1J9WWTUUMnfa2PWaPU3qPND7GJN>
  
  • Kaggle: <https://www.kaggle.com/learn>
  
  **Cycle 2: Learning how to Program in Python**
  
  • Python Course for Beginners: <https://www.youtube.com/playlist?list=PLyqOvdQmGdTSEPnO0DKgHlkXb8x3cyglD>
  
  • Kaggle: <https://www.kaggle.com/learn>
  
  • CodingBat: <https://codingbat.com/python>
  
  • Project Euler: <https://projecteuler.net/>
  
  **Cycle 3: Learning how to use Python for Data Analysis and learning Statistics**
  
  • Python Course for Machine Learning and Data Analysis: <https://www.youtube.com/playlist?list=PLyqOvdQmGdTR46HUxDA6Ymv4DGsIjvTQ->
  
  • Khan Academy: <https://pt.khanacademy.org/math/em-mat-estatistica>
  
  • Pandas, Matplotlib and NumPy:
  
  <https://www.youtube.com/playlist?list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS>
  
  <https://www.youtube.com/playlist?list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB>\_
  
  <https://www.youtube.com/playlist?list=PLzgPDYo_3xukqLLjNeuCxj4CwvkJin03Z>
  
  • How to go from ZERO to Data Science in Just One Lesson: <https://www.youtube.com/watch?v=WJE4spsP-Xk>
  
  **Cycle 4: Learning basic concepts about Machine Learning and Probability**
  
  • Course Introduction to Machine Learning and Machine Learning Algorithms:
  
  <https://www.youtube.com/playlist?list=PLyqOvdQmGdTSqkutrKDaVJlEv-ui1MyK4>
  
  <https://www.youtube.com/playlist?list=PLyqOvdQmGdTS4PcZsAKhXgkdUyLjXnptS>
  
  • Machine Learning with Python and Scikit-learn: <https://www.youtube.com/playlist?list=PLeFetwYAi-F9Z0N3ZSjPL2i24PO5iiXaY>
  
  • Kaggle: <https://www.kaggle.com/learn>
  
  • Khan Academy: <https://pt.khanacademy.org/math/em-mat-probabilidade>
  
  • Google Machine Learning Crash Course: <https://developers.google.com/machine-learning/crash-course>
  
  **Cycle 5: Learning Applications of Machine Learning in Chemistry**
  
  • EMMSB LNCC: <https://www.youtube.com/playlist?list=PLIJhet1J-_l82wpFzn2ZOyCIKy-FotuND>
  
  • The Computational Toolkit: <https://www.youtube.com/playlist?list=PLgCwaJhZsSlMQky78U5JLyMOgO16o6ubE>
  
  • Learn how to use RDKit or DeepChem, a collection of cheminformatics and machine learning tools for Python
  
  **Cicle 6: Learning Deep Learning Fundamentals**
  
  • Deep Learning by Andrew Ng on Coursera: <https://www.coursera.org/specializations/deep-learning>
  
  • Kaggle:
  <https://www.kaggle.com/learn>
  
  **Tags:** machine-learning, python
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-04-20. Score: 2 | Accepted answer
  
  I graduated with a university degree in Artificial Intelligence, I only covered Artificial Intelligence a little bit on it, surprisingly, but this brings me to the actual point of my answer. I have started a bit here and there to try and learn myself I mainly use the site <https://datacamp.com> a lot if I am honest, as for Python when I learned this I tried to stick to one resource and learn algorithms and problems later. This is a good resource on Python: <https://roadmap.sh/python> and also this course is good which is free: <https://futurecoder.io/>
  
  I mean your list is good, but what I would say try and find overlaps more in what you are learning because especially I find myself with programming say doing a Python programming course at beginner level, then doing another at beginner level but 'data science' you will find a lot of overlap.
  
  
  ---
  
  