Path for Data Science and Machine Learning

AI_path.md

Should I need to start over, I would pursue this path to first grasp the fundamentals of Python for Data Science, Machine Learning and understand the core concepts:

• Learn basics of Python [https://www.youtube.com/watch?v=rfscVS0vtbw&ab_channel=freeCodeCamp.org]
• How to use Python for Data Science (basics) [https://www.freecodecamp.org/news/learn-pandas-for-data-science/]
• Mathematics for ML [https://www.youtube.com/watch?v=0z6AhrOSrRs&ab_channel=MyLesson]
• Data Analysis with SQL [https://www.udemy.com/course/the-complete-sql-masterclass-for-data-analytics/]
• Data Analysis and Visualisation in Python [https://youtu.be/GPVsHOlRBBI?si=ZXbHFUaCHELBSIy4]
• Statistics Essentials (inferential Statistics mostly) with Hypothesis Testing
  • [Book: https://www.oreilly.com/library/view/practical-statistics-for/9781491952955/]
  • https://www.khanacademy.org/math/statistics-probability
• Exploratory Data Analysis [https://youtu.be/fRVXW_oCTj0?si=XdoWHZdQhyXzL7BH]
• Machine Learning [https://youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU&si=QsJESl_T5V0TBpeK]
  • Supervised Learning
    • Linear Regression
    • Logistic Regression
    • Naive Bayes - Bayes’ Theorem
    • Advanced Regression
    • Support Vector Machine
    • Tree Models - Decision Tree, Random Forests
    • Boosting algorithms - AdaBoost, XGB
  • Unsupervised Learning
    • K-means Clustering
    • Hierarchical Clustering
    • Principal Component Analysis (PCA)
• Deep Learning [https://youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb&si=Qk2h3KSMnQQBhZVx]
  • Introduction to Neural Network (NN)
  • Feed forward in NN
  • Back-propagation in NN
  • Convolutional NN
  • Recurrent NN
• NLP and Audio Analysis [https://huggingface.co/learn]
• To practice the concepts learned I would recommend https://www.kaggle.com