Start Learning Ml and Deep Learning With Python
If you’re a self-taught programmer, you’re probably familiar with the struggle of deciding which direction to upskill in. After all, trends in the job market fluctuate, making it challenging to pin down what skills employers will want even after a few months spent learning that new skill.
That said, the pandemic has spurred a demand for emerging technologies. This is especially true for AI and its subsets –– namely, machine and deep learning. CIO Dive reports that a third of tech job posts in October 2021 involved emerging tech, and this trend is only expected to continue this year. Consequently, those looking to enter the AI space can be sure that related skills will remain in demand as the industry continues to grow and change.
If you’re interested in pursuing work in the field and just getting started, you might want to consider beginning –– as many do –– with Python. Below, we’ll dig into this approach a little bit more.
Python recently surpassed Java as most in-demand programming language — and for good reason. This high-level, open-sourced language is highly versatile. It’s used in everything from rapid application and network development to data science and AI. Since its syntax mimics the English language, it’s also easy to learn and maintain. And for that matter, experienced programmers need not learn Python from scratch. World-renowned programming columnist Yashavant Kanetkar emphasizes in his highly-rated book “Let Us Python” that every language shares common ground with others. For example, Python and Java are both object-oriented and have similar procedural programming concepts. This is something you can leverage when adding a new language to your programming arsenal. Ultimately though, Python’s ease of use, extensive library, and compatibility with various systems makes it an ideal choice for AI applications.
Experiment with data science applications
Since AI aims to analyze data and generate insights, learning how to organize and visualize data can help you more effectively utilize it in ML models. It’s also important to have a data science background today, with many enterprises — including startups — integrating this discipline with ML to more effectively leverage data. This is something that Sifted correctly points out a lot of startups are struggling with, and experience managing data will help to alleviate those struggles.
Some important data science Python libraries include NumPy, pandas, and MatPlotLib. NumPy provides multidimensional array objects that can be used in various mathematical and statistical operations, while pandas offers structured data that can help you learn data formatting. Finally, MatPlotLib visualizes findings with aids like graphs. Online classes from sites like freeCodeCamp can teach you how to use these libraries to manipulate and properly analyze data.
Branch out into ML and deep learning
Crucially, you can now learn to use Python for ML mini end-to-end projects of your own. First, identify a problem you want to address — such as teaching an ML model how to read handwriting. Train the algorithm with relevant data sets, in this case likely thousands of examples of handwritten digits and letters. Run the program, evaluate the results, and improve your code as needed.
If you need a more comprehensive guide, “Hands-on Machine Learning” by ML consultant Aurélien Géron is full of examples you can refer to. It also covers deep learning, which you may want to tackle next. We’ve previously mentioned that neural networks, which form the basis of deep learning algorithms, can be programmed with libraries like NumPy and Tensorflow. Both involve applying operations to vectors, so consider refreshing your knowledge on algebra and calculus before diving into the Tensorflow tutorial on our site!
Ultimately, it may take a while to perfect ML and deep learning with Python. That’s okay — jobs in AI will be in demand for the next decade or more. Take your time. Once you’re confident in your newfound skills, you can start building your portfolio with increasingly larger projects, and likely secure excellent work in the field. You’ll be in perfect position to help usher the world further into the Digital Age.