| The current programming language dominating the ML community is **Python**. Not surprising, since the ease of use allows you to focus on writing efficient code without needing to spend too much time learning the intricacies of the language's syntax. There's a good chance you already know Python, but we'll go over the basic steps anyway. | The current programming language dominating the ML community is **Python**. Not surprising, since the ease of use allows you to focus on writing efficient code without needing to spend too much time learning the intricacies of the language's syntax. There's a good chance you already know Python, but we'll go over the basic steps anyway. |
| 1. Learn the Basic Syntax and Data Types You'll need to familiarize yourself with Python's syntax, variables, data types (integers, floats, strings, lists, dicts), and basic operations (arithmetic, string manipulation, indexing, slicing). 2. Control Flow Understand conditional statements (`if`, `elif`, `else`), loops (`for`, `while`), and logical operators (`and`, `or`, `not`). Very important for implementing decision-making and repetition in your code. 3. Functions and modules Learn how to define and use functions to encapsulate reusable blocks of code. Also, you'll need to understand how to import and utilize modules (libs). 4. Data Structures and Manipulation Get yourself acquainted with fundamental data structures like lists, tuples, sets, and dictionaries. Learn how to manipulate and transform data. 5. NumPy A fundamental library for scientific computing in Python. You will need to gain proficiency in using NumPy arrays for efficient numerical computations. 6. Pandas You will often need Pandas DataFrames to clean, transform, filter, aggregate, and analyze your datasets. 7. Plotting and Data Visualization Become familiar with libraries such as [Matplotlib]([[https://matplotlib.org/|https://matplotlib.org/]]) and [Seaborn]([[https://seaborn.pydata.org/|https://seaborn.pydata.org/]]) for creating plots, charts, and visualization. Not strictly necessary, but recommended. | 1. Learn the Basic Syntax and Data Types You'll need to familiarize yourself with Python's syntax, variables, data types (integers, floats, strings, lists, dicts), and basic operations (arithmetic, string manipulation, indexing, slicing). 2. Control Flow Understand conditional statements (`if`, `elif`, `else`), loops (`for`, `while`), and logical operators (`and`, `or`, `not`). Very important for implementing decision-making and repetition in your code. 3. Functions and modules Learn how to define and use functions to encapsulate reusable blocks of code. Also, you'll need to understand how to import and utilize modules (libs). 4. Data Structures and Manipulation Get yourself acquainted with fundamental data structures like lists, tuples, sets, and dictionaries. Learn how to manipulate and transform data. 5. NumPy A fundamental library for scientific computing in Python. You will need to gain proficiency in using NumPy arrays for efficient numerical computations. 6. Pandas You will often need Pandas DataFrames to clean, transform, filter, aggregate, and analyze your datasets. 7. Plotting and Data Visualization Become familiar with libraries such as [Matplotlib]([[https://matplotlib.org/|https://matplotlib.org/]]) and [Seaborn]([[https://seaborn.pydata.org/|https://seaborn.pydata.org/]]) for creating plots, charts, and visualization. Not strictly necessary, but recommended. |