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Python Resources

To have a solid foundation in certain key areas. Here's a list of essential concepts and skills you should familiarise yourself with:

  1. Basic Python Programming:
    • Understand variables, data types, and basic operators.
    • Learn control flow statements (if, else, elif, loops).
    • Familiarize yourself with functions and how to define and call them.
  2. Data Structures in Python:
    • Know how to work with lists, tuples, sets, and dictionaries.
    • Understand indexing and slicing for accessing elements in data structures.
  3. File Handling:
    • Learn how to read from and write to files in Python.
  4. Functions and Modules:
    • Understand the concept of functions and how to define and use them.
    • Learn how to organize code into modules for better code organization.
  5. NumPy:
    • NumPy is a fundamental library for numerical computing in Python. Learn how to work with arrays and perform basic operations.
  6. Pandas:
    • Pandas is a powerful library for data manipulation and analysis. Learn how to work with data frames, handle missing data, and perform data aggregation.
  7. Matplotlib and Seaborn:
    • Familiarize yourself with these libraries for data visualization. Learn how to create various types of plots to explore and present data.
  8. Basic Statistics:
    • Understand fundamental statistical concepts such as mean, median, standard deviation, and variance.
    • Learn about probability distributions and basic statistical tests.
  9. Command Line/Console Basics:
    • Understand basic command line or console operations. This includes navigating directories, running scripts, and managing files.
  10. Version Control with Git:
    • Learn the basics of Git for version control. This is important for tracking changes in your code and collaborating with others.
  11. Jupyter Notebooks:
    • Familiarize yourself with Jupyter Notebooks, an interactive environment widely used in data science and machine learning. It allows you to write and execute code in a modular and interactive way.
  12. Basic Machine Learning Concepts:
    • Before delving into machine learning, have a high-level understanding of basic concepts such as supervised learning, unsupervised learning, overfitting, underfitting, and model evaluation metrics.

By covering these foundational concepts, you'll be better equipped to understand and apply machine learning techniques using Python libraries like Scikit-Learn, TensorFlow, or PyTorch. Online tutorials, courses, and practical projects can be excellent resources for gaining proficiency in these areas.

Python Basics SyllabusPython Basics SyllabusModule-I: Basics of Python Programming: Features of Python, Literal Constants, variables and identifiers, Data Types, Input Operation, Comments, Reserved Words, Indentation, Operators and expressions, Expressions in Python, Operations on Strings, Type Conversion Module-II: Decision control Statements: Introduction to Decision Control Statements, Selection/Conditional Branching Statements, if Statement, if-else Statement, Nested if Statements, if-elif-else Statement, Basic Loop Structures/ Ite