How much python is required for machine learning?

how much python is required to learn machine learning

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Python is one of the world’s most preferred programming languages for machine learning. It is simple, consistent, flexible, easy to understand, and has multiple libraries and frameworks. 

The most common question among people looking to upskill in machine learning is: how much python is required for machine learning?

Or if they do have an answer to this, they don’t know what to learn in Python for machine learning. 

Still, some are confused about which language to learn, while others struggle with what to learn in Python.

In this blog, you will get to know the answer to all these questions.

 

How much Python is required to learn machine learning?  

Python has various functions and libraries; you don’t need to master all of them to learn machine learning. For beginners, it’s suggested to start with the basic concepts of Python. While for experts, it’s suggested to start with libraries for data visualization and analysis. Learning these things is enough to get started in the field of ML.

Let’s find out which basic concepts are essential for beginners.

Basic Python

Before you start to learn libraries, make sure to master the following topics :

  • Variable: Variable refers to the specific name or symbol that stores the particular value.
  • Comparison operations: (= =), ( ! = ), ( < ), ( >) – these are some comparison operators used in python. Data comparison is one of the basic functions of machine learning.
  • Strings: Strings are the arrays of bytes that represent Unicode characters. In python, a string is a handy feature denoted by “str”.
  • Lists, tuples and dictionaries(e.g. -, >, <, ==) – Lists, tuples and dictionaries are data collection techniques. These three components store and organize data in python.
  • Boolean operators and boolean expressions (e.g., and, or, and not ): Primarily, python has three operators, AND, OR and NOT. These operators are used to represent the values of expression.

Apart from this, loops and functions are other basic concepts that you can’t miss if you want to master python.

 

Python Libraries

Libraries are the real magic of python. Most of the libraries are free to use and helpful in data analysis. Because of their varied application, python libraries find their application in key areas of machine learning like data interpretation.

  1. Pandas 

Pandas is one of the most important libraries used in machine learning. It is a python toolkit best suited for data types like tables, time series, and matrix data. Since machine learning is all about data analysis you should learn how to use Pandas.

You must learn to do the following tasks using Pandas :

  • importing data from external sources
  • creating data frame
  • formatting data frames (i.e., creating or changing indexes)
  • filtering data
  • aggregating data
  • reshaping data

 

  1. NumPy

NumPy, also known as Numerical Python, is a library that only works with numerical data and arrays. This library is the same as Pandas in function; the only difference is its multidimensionality.

To master ML you must learn the following concepts of Numpy:

  • Sum the values, the nymph array
  • Calculating the mean
  • Finding Median or standard deviation
  • Reshaping NumPy array
  • Computing algorithms, exponentials, etc

  

  1. SCIKIT LEARN

Scikit learn is known as the most widely used open-source data analysis library. Skit learning helps implement various machine learning functions, such as data analysis and mining.

You must learn the following concepts in order to use scikit learn:

  • Make algorithmic-based decision 
  • Regression and predicting the value attributed associated with the object
  • Model selection and preprocessing

 

  1. Seaborn

Seborn is known for data visualization and that’s why people prefer mastering it before indulging in the intricacies of python. Seaborn helps to understand, explore, and present data through semantic mapping and statistical aggregation.  

It is recommended to learn the following graphs:

  • Seaborn Data Visualization Library
  • Line lot
  • Bar Chart Plots
  • Histograms Plots
  • Scatter Plots

  1. MatplotLib

MatplotLib is a visualization library in python. Its plotting library is widely used to create interactive visualizations. The library is used to make graphs and plots. Therefore, don’t forget to learn the following concepts :

  • Line Chart
  • Pie Charts
  • Histograms
  • Scatter Plots
  • Box Plots

What to learn in Python?

The first and foremost thing you should learn in Python is how to perform data analysis. GeekforGeeks says there are a total of six steps: Data collection, data cleaning, data analysis, and data diagnostics. 

Let’s find out how Python is used in each of these steps.

Data cleaning

Mostly, Pandas and NumPy are used to clean data so it is important for you to learn these in order s to clean data sets. Focus more on how to drop unnecessary information, as our goal is to remove fluff. 

Data cleaning is known as the process of refining data. Data analysts find the irregularities/ null values and then replace them with other values. It’s considered the second phase of developing any machine learning model.

Data visualization

Data visualization represents data sets through chart diagrams and pictures. Some forms are histograms, pie charts, bar graphs, line graphs, and maps. Matplotlibs, Seaborn, and Plotly are popular data visualization libraries that are used in this step.

Model Evaluation

Model Evaluation refers to the process when the model is tested. Developers test the model in multiple conditions, record the results and then use visualizations to find the defect.

Model Evaluation requires using visualization libraries such as Seabron and Matplotlibs. Therefore you should learn how to do the model evaluation using these libraries. 

 

Conclusion

How much python is required for data learning? –  the answer is all the basic concepts and a few python libraries.  

The goal is to learn how to perform data collection, data interpretation, data wrangling, and data visualization, all the things which are extensively required in ML. These are the fundamental skills you need to master in order to give an easy start to your career in ML. Evaluate how much you already know, and what you need to learn.

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