Machine learning (ML) is one of the fastest-growing fields in the US business landscape today. There is a surge in demand for predictive analytics, as companies improve their decision-making procedures across various sectors. Reports also indicate a projected 42% growth between 2020 and 2027, due to a growing shift towards data-driven decision-making. The trend will continue across various industries, including healthcare, finance, retail, and others.
Python is also a helpful tool for those learning machine learning due to its vast libraries, readability, and extensive community support. The simple syntax makes it more convenient to learn and understand than many other languages. If you’re looking to build your career in machine learning with Python, keep reading!
What is Machine Learning with Python?
Machine learning using Python is a natural pathway due to the latter’s simplicity, extensive libraries, and robust community support. Python offers ample dynamism to developers at varying levels and enables the development of multiple applications. The simple syntax makes it ideal for team-wide collaboration and coding algorithms while simplifying data validation.
The entire scraping, processing, refining, cleaning, and arranging systems are made easier with Python. It is more flexible in terms of pairing with other programming languages for completing ML models. Python can also run on almost any OS, from Windows to Mac, Unix, Linux, and others.
Also Read: What is Predictive Analytics and Its Role in Business Strategies?
Why Choose Python for Machine Learning?
Why combine Python and machine learning? Here are some compelling reasons to use it for the latter.
- Ease of Learning- Python has an easy-to-understand syntax that is highly intuitive and hassle-free. OOP also offers a more logical approach to processing, organizing, and planning code.
- Vast Libraries- Python stands out from its peers with its extensive system of libraries and frameworks. These are tailored for machine learning, making it convenient for developers to execute the algorithms as well.
- Robust Industry Relevance – One of the biggest differentiators for Python is its cross-platform compatibility. It has led to widespread flexibility and adoption across industries, enabling the same code to be used across platforms.
Also Read: What is Gradient Boosting in Machine Learning?
Key Python Libraries for Machine Learning
Have a look at the table below to understand the major libraries for deep learning with Python.
| Library | Purpose | Key Features |
| NumPy | Numerical computing with arrays | Fast array operations, supports multi-dimensional arrays |
| Pandas | Data manipulation and analysis | DataFrames, handling of missing data, built-in plotting |
| Scikit-learn | General ML algorithms | Simple syntax and ideal for beginners |
| TensorFlow | Deep learning and neural networks | Scalable and production-ready |
| Keras | High-level API for TensorFlow | Easy to use, quick prototyping |
| PyTorch | Deep learning | Dynamic computation graphs, flexibility |
Basic Concepts of Machine Learning
Here are some basic concepts of machine learning with Python worth knowing about:
- Supervised Learning- Using labeled data (corresponding output and input) to train models and make predictions on unseen or new sets.
- Unsupervised Learning- These models operate on unlabeled data, identifying structures and patterns without predefined outputs.
- Classification- A specific supervised learning model that predicts any categorical outcome (in a category or class) for any input. For instance, you can identify whether an email is spam or not.
- Regression- It is a specific supervised learning model that predicts a continuous value (a score or number) for the input.
- Clustering – In this system, an unsupervised learning tool groups data points into several clusters based on their similarity.
Also Read: Top Machine Learning Tools Used by US Tech Companies in 2025
Setting Up Your Python Environment for ML
Here’s how to set up the Python environment for machine learning.
- Installing Python- Download the latest version of Python 3.x from https://www.python.org and check Add Python to PATH in the setup.
- Choosing a Code Editor (IDE)- It’s now time to set up a code editor or IDE like Jupyter Notebook to make coding more straightforward and more convenient.
- Setting Up the Virtual Environment- Create a clean environment where you only install packages needed by the project. Use python-m venv ai_env to create it and activate it on your chosen OS (ai_env/Scripts/active for Windows). Deactivate once done.
- Installing Core AI and ML Libraries- In this step, you’ll install popular libraries such as Scikit-learn, NumPy, and Pandas.
- Testing- Test the setup, and if the script runs without errors, you’ll be good to go!
Also Read: How to Learn Machine Learning Online in the US
First Steps: Writing Your First ML Model in Python
Ready to write your first ML model in Python? Here’s a Python machine learning example that will help you get a better picture of what’s in store. Suppose you want to predict the pass/fail likelihood for a student. In this case, you may have gathered data already, such as the following-
| Hours Studied | Passed Exam? |
+——————-+——————+
| 2 | No |
| 4 | No |
| 6 | Yes |
| 8 | Yes |
Use the Python NumPy library to build two arrays to store the columns-
Import numpy as np
# Step 2: Prepare the data
# X = input (features), y = output (labels)
X = np.array([[2], [4], [6], [8]]) # Hours studied
y = np.array([0, 0, 1, 1]) # 0 = Fail, 1 = Pass
Choose a model, which can be a machine learning algorithm, when the output prediction is true/false, yes/no, or even pass/fail. A LogisticRegression model may be used here from sklearn or any other library to train the collected data. Now, train the model- model.fit(X, y) and test it by making a prediction-
Hours_studied = np.array([[5]]) #Studentstudied5hours
Prediction= model.predict(hours_studied)
Print(“Prediction (0=Fail, 1=Pass):”, prediction [0])
Tips for Beginners to Succeed in ML with Python
Here are some tips that may help you ace machine learning with Python.
- Get Proper Clarity- Ask yourself why you wish to learn ML and build a career in this space. Identifying the reasons or motivation for your choice will help you maintain focus in the face of challenges.
- Brush Up on Statistics and Mathematics- Machine learning depends massively on mathematics, and you should have the right foundation in linear algebra (vectors, matrices, etc.).
- Learn Programming with Emphasis on Python- Why Python? It’s the primary ML language due to its library ecosystem and simple syntax.
- Data Manipulation Aspects- You can practice data loading, cleaning, and even visualization with Matplotlib and Pandas.
Also Read: Best Online Machine Learning Courses for Working Professionals
Advance Your Machine Learning Skills with upGrad
upGrad is your one-stop partner for learning ML, data science, and programming languages. You’ll discover several flexible and affordable programs from leading institutions worldwide here. What’s more, they offer an industry-ready curriculum and excellent prospects in addition to dedicated guidance and student support.
Some popular Machine Learning AI programs available on upGrad:
- Post Graduate Certificate in Generative AI (E-Learning)
- Master of Science in Machine Learning & AI
- Executive Certificate in Generative AI for Leaders (E-Learning)
- Executive Diploma in Machine Learning and AI with IIIT-B
FAQs on Machine Learning with Python for Beginners
Q: What are the best resources to learn ML with Python?
Ans: Top resources include learning platforms like upGrad, which offer structured programs for beginners and professionals. Additionally, hands-on practice with libraries such as Scikit-learn, Pandas, Keras, and TensorFlow can help build a strong foundation in machine learning with Python.
Q: How long does it take to learn ML with Python?
Ans: It can take a few months or a year, depending on your proficiency and skills. On average, professionals usually take 2-6 months to grasp the fundamental concepts and get started.
Q: Do I need a strong math background to learn ML?
Ans: Yes, you should have a strong foundation in probability, linear algebra, and calculus to learn machine learning.
Q: Which Python libraries are best for ML?
Ans: Some of the top options for Python libraries include the following:
- Scikit-learn
- Pandas
- Keras
- NumPy
- TensorFlow
Q: What is machine learning, and how does Python help?
Machine learning is an AI field where systems learn from available data and improve performance without requiring explicit programming. Python is the most popular language for machine learning (ML) due to its readability, extensive libraries, and overall simplicity.
