In the past couple of years, Python has gained massive popularity. One would argue that it’s because the language is easy to learn. Well, that could be a valid reason but there is more to it. Python is widely known for its diversity. By just learning Python, you could become a web developer, cyber security expert, automation tester, game developer, data analyst, machine learning engineer, you name it. The language just comes as a one-size-fits-all for programmers. This is one of the reasons TIOBE’s index ranked it the #1 programming language in October 2021 and has remained the top to date. In my opinion, you are indecisive about which programming language to learn, Python is a safe bet. You can decide to specialize when you get comfortable with the language.
In this article, we will discuss the steps required to become a Machine learning Developer with Python. We’d begin by understanding who a Machine Learning Developer is.
Who is a Machine Learning Developer
A machine learning developer is someone who uses machine learning models to train and optimize machine learning models. To be clear, a machine learning model can be seen as a black box that receives input data (features) to make predictions (labels). As a machine learning engineer, you will be dealing with data a lot. From data sourcing, data cleaning, exploratory data analysis, feature engineering, feature scaling, and so on. This is why Machine Learning and Data Science are intersecting fields. You would also be working with machine learning algorithms to build the models.
So how do you become a Machine Learning Developer?
Step 1: Learn Python
Python is the number one language for machine learning. If you are a complete beginner, Python is a great place to start. Some of the key concepts you must understand include:
- Variables, and datatypes
- List slicing and list comprehension
- Control flow
- Conditional Statements
- Lambda functions
- Working with files
- Data scraping with Beautiful Soup.
- Object-oriented programming
You can learn all these by joining a Python online training where you will be taught the fundamentals of Python.
Step 2: Learn how to use Numpy and Pandas
Learning Python is broad and it is impossible to know how to work with all the libraries. When you want to specialize in Machine Learning, however, some Python libraries are super important. Numpy and Pandas are typically used for data manipulation and exploratory data analysis. This plays a critical role when you want to understand your data before pushing it into a machine learning model.
Step 3: Learn Matplotlib and Seaborn
Matplotluib and Seaborn are some of the most popular visualization libraries in Python. As a Machine Learning Engineer, you will need to perform some visualization to aid storytelling and better understand your data. For instance, you can quickly check for the presence of an outlier by drawing a boxplot. You can check the relationship between features using correlation plots. You can check the distribution of a feature using histograms and so on. These visualizations are critical in better understanding the broadness of the data and how best to deal with it.
Step 4: Learn Scikit Learn
Scikit Learn is one of the most popular machine learning libraries. It is basically used for building machine learning models. The library has numerous machine learning algorithms that you only need to import and instantiate to get started with. Also, the sci-kit learn library allows you to evaluate the model using whatever metric you desire. For classification problems, you can use accuracy, precision, recall,f1 score, etc. While for regression problems, you can use root mean squared error, absolute mean square error, and so on. All these can be called and used with the sci-kit learn library.
Step 5: Learn Keras, Tensorflow or Pytorch
Keras is a Python-based API developed by Google used for building deep learning models. It is built on top of Tensorflow, which is quite low-level for beginners. Keras is, however, much more straight. You can easily build a deep learning model with a few lines of code. While you can also build deep learning models with sci-kit learn, Keras is preferable because of its ease of use. Since its inception, Keras has become increasingly popular. Now, it holds many transfer learning models that can be used right away. You only need to change the input and output layer and you get an almost perfect deep learning model.
It is important to point out that Pytorch is another great Python library for building deep learning models. However, Pytorch is quite low-level as well and building models can be tedious for a beginner. Nevertheless, Pytorch has a great community and use case. It was built by Facebook and is equally as effective as its Keras counterpart.
You could as well decide to learn either Keras or Pytorch. But learning both may be unnecessary as a beginner.
Wrapping up
In this article, you have learned how to become a Machine Learning Engineer by learning python. Once you are comfortable with the fundamentals of the language, there are some key Python libraries you must know. To recap, you need to learn Beautiful Soup, Numpy, Pandas, Matplotlib, Seaborn, sci-kit learn, and Keras (or Pytorch). Note that there are many other great Python libraries that you can learn as you progress. However, these are really great libraries to get you up and running as a Machine Learning Developer. You can learn all these by enrolling in an online Python course with a specialization in Machine Learning.
