Machine learning is the new buzzword of this era. You may have seen hundreds of advertisements in your social network, or maybe some of your friends are showcasing this skill to enter the cool kid’s zone, or maybe you have seen ‘The Terminator’ movie way too much time, but your interest in this topic is genuine as machine learning using python is currently one of the most sought after career in the job industry.
In this blog on machine learning with Python, we will shed some light on the essential concepts of machine learning using Python and how machine learning can be done using this favorite programming language.
What Exactly Is Machine Learning?
Machine learning (ML) is a subset of Artificial Intelligence (AI).
As the word itself says, it’s Machine + Learning.
Ask yourself a simple question: if you show a book to a toddler and ask him/her what is it, can they identify what it is? The answer is a big No, but if the same book is shown to you, can’t you identify it as a book within a fraction of a second!
So, what changed? What is the difference between that toddler and you? Put, over the years, you ‘learned’ that it is a book. Suppose humans can learn why machines can’t learn the same. And Machine learning does that exactly!
Machine learning is a part of artificial intelligence. A program can identify a recognizable pattern by exploring a large set of data that humans cannot identify.
Just like the knowledge of a five years old isn’t as accurate as 25 years old. Similarly, the accuracy of the prediction of a Machine learning model depends on the amount of explored data.
What Are The Types Of Machine Learning?
Machine learning can be generally classified into three categories:
- Supervised Machine Learning
- Unsupervised Machine learning
- Reinforcement / Semi-Supervised Machine Learning
The type of machine learning in the above classification is based on the type of data. When the data type is labeled, it falls under Supervised ML.
When unlabeled, it comes under unsupervised ML, and when the database contains both labeled and unlabeled data, it comes in the purview of Reinforced machine learning.
What Are The Real-world Applications Of Machine Learning?
There are numerous amounts of real-world applications of ML, and the sky’s the limit. Some of the most common everyday applications are as follows:
- Facial recognition
- Spam detection in email
- Music and movie recommendations by Netflix, Gaana, Prime video, etc.
- Text and speech recognition
- Online shopping recommendations and many more.
Also Read: Is Full-Stack Development Hard to Learn?
Machine Learning Using Python
In Machine learning, for predicting any result, we need a data set.
The data set is then divided into two sets, one for training the model and another for testing if the model is working correctly.
Now Machine learning programs can be built in many different programming languages and platforms, but Python is one of the most preferred ones.
The reason is in Python, and you don’t need to program a model explicitly. You can use a sound library that comes with various pre-loaded models.
Steps In Executing A Project On Machine Learning Using Python
Step-1: Import Data
As stated earlier, an ML project needs data to identify a model. Those data can be loaded in the form of a CSV file. If you want a sufficient amount of raw data that you want to use in learning, you can get it for free from a website called ‘Kaggle.’
Step-2: Data Cleaning
The model learns from the data it is fed, so if there is ‘bad’ data in the data set, the prediction of the model will be wrong too.
So before processing, the data needs to be cleaned. Any missing value to be removed, and if there are qualitative data in the dataset, it needs to be encoded to convert it to quantitative data.
Step-3: Data Split
The dataset needs to be divided into two sets, one for training the model and another for testing the model’s accuracy.
Step-4: Creating a Model
Import the correct algorithm from a python library depending on the type of problem your project is trying to solve and the type of available data. There are various libraries, Sci-kit learn being the most popular one.
Step-5: Train the Model
The next step is to feed the model with data to analyze and identify the required pattern.
This is the primary reason for building a machine learning using python project. After the model is trained with the input data, it can successfully make predictions for a given problem.
The accuracy of a problem is subjective to the accuracy and amount of input data. Hence, an accuracy test is done with the test data set to confirm if the model is a good fit or not.
Libraries Required For A Project Of Machine Learning Using Python
Building a machine learning project with Python requires knowledge and access to various libraries; some of the most popular and essential ones are as follows
- Jupyter Notebook
- Sci-kit learn (for various ML models)
All these libraries can be installed individually, but the best practice is to download and install ‘Anaconda’ for the appropriate version of Python. The beauty of Anaconda is it installs all the required libraries in a single go.
Till now, machine learning might be feeling overwhelming to you, but in reality, it’s pretty simple and easy as long as you know what you are doing.
You do not need to write thousands of lines of code thanks to Python. Let’s look at how a real-life python program for machine learning looks
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
smaple_data = pd.read_csv (‘sample.csv’)
X = sample_data.drop (columns =[ ‘output’ ])
y= sample_data [ ‘output’ ]
model = DecisionTreeClassifier ()
model.fit (X, y)
model.predict (  )
Looks simple enough? If you’re interested and aspire to learn more and gain some practical experience, head over to Machine Learning using Python free course from Great Learning, where you can gain an in-depth understanding of the subject with a practical approach.