Skills Required for Applied Machine Learning with Python: A Detailed Guide In 2022

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Published on July 16th, 2022

Machine learning has been quietly revolutionizing our lives for the last ten years. From taking selfies with a blurred background and a face in focus to having virtual assistants like Siri and Alexa answer our questions, we increasingly depend on products and apps that implement machine learning at their core.

In simpler terms, machine learning is one step in artificial intelligence. Machines learn using machine learning. How exactly? Just like people learn through training, experience, and feedback.

Once machines learn through machine learning, they implement the knowledge gained for many purposes, including but not limited to triage, diagnostics, robotics, analytics, and prediction in many fields.

These implementations and applications have made machine learning an in-demand skill in programming and technology.

Table of Contents

What is Applied Machine Learning?

Applied machine learning is the application of machine learning to a specific data-related problem. This machine learning can include either supervised models, meaning that the algorithm improves itself based on labeled training data, or unsupervised models, in which inferences and analysis are drawn from unlabeled data. Applied machine learning is generally characterized by statistical algorithms and techniques for understanding, categorizing, and manipulating data.

Machine learning can be used where there are non-deterministic elements of the problem, especially where the manipulation and analysis of large amounts of statistically generated data are required.

Technical skills required for applied machine learning

1. Applied mathematics

Math is a pretty important skill in a machine learning engineer’s arsenal. It’s also one of the core subjects taught directly in school, which is why it’s the first skill on our list. But why does one need math at all, one asks? (Especially if one doesn’t like it?!!) Mathematics can have many uses in ML. One can use various mathematical formulas when choosing the suitable ML algorithm for oner data. One can use mathematics to set parameters and approximate confidence levels; many ML algorithms are applications derived from statistical modeling techniques, so they are straightforward to understand. and have a strong foundation in mathematics. Some important math topics one needs to know include linear algebra, probability, statistics, multivariate calculus, distributions like Poisson, normal, binomial, etc. Apart from math, it can also be beneficial to have some knowledge of physics concepts if one wants. To become a machine learning engineer.

2. Basics of computer science and programming

This is another essential requirement to become a sound machine learning engineer. One needs to be familiar with various CS concepts like data structures (stack, queue, tree, graph), algorithms (search, sort, dynamic and greedy programming), space and time complexity, etc. The good thing is that one probably knows all these if one did a bachelor’s degree in computer science! One should be well versed in various programming languages ??like Python for data preprocessing, etc. Python is a modern programming language primarily for Machine Learning and Data Science, so it’s great if one can be well versed in its libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, etc.

3. Machine learning algorithms

What is the essential skill to become a machine learning engineer? Obviously, it is necessary to know all the standard machine learning algorithms to understand where to use which algorithms. So it is good if one has good knowledge about all these algorithms before starting one’s journey as an ML engineer.

4. Modeling and evaluation of data

As a machine learning engineer, one should have experience with data modeling and evaluation. Data modeling involves understanding the underlying structure of data and finding patterns that are not obvious to the naked eye. One must also evaluate the data using an algorithm that is appropriate for the data. A classification algorithm suitable for big data and speed can be naive, or a regression algorithm for accuracy can be a random forest. Similarly, the clustering algorithm for categorical variables is k mode, while for probability, it is k.

5. Neural networks

No one can forget the importance of neural networks in the life of an ML engineer! These neural networks are modeled after neurons in the human brain. They have multiple layers, including an input layer that receives data from the outside world, which then passes through several hidden layers that transform the input into valuable data to the output layer. These demonstrate deep insight into parallel and sequential computations used to analyze or learn from data. There are various types of neural networks: feedforward neural networks, recurrent neural networks, convolutional neural networks, etc. Although it is not necessary to understand all these neural networks in detail to become an ML engineer, one must know the essential basics. And one can always learn the rest along the way.

Here are some top skills required in 2022 for applied machine learning in Python:

We are discussing Python only because in 2022, Python is a widely used language in data science and machine learning, and it’s one of the high-on-demand data science tools for beginners. It is a widely helpful programming language focused primarily on clarity and simplicity. If one is not a developer but rather hoping to learn, it’s an incredible language to start with. It’s simpler than other widely applicable dialects, and there are various tutorials that even non-software engineers can learn. With Python, a flexible and widely helpful programming language, one can perform multiple tasks such as time series analysis or sentiment analysis. One can spread open data collections and do things like sentiment analysis of Twitter accounts.

Various components of Python make it the preferred language for machine learning. Such elements are discussed below:

Numpy
Pandas
matplotlib
seaborn
Scikit-Learn
TensorFlow
nltk

1. NumPy

NumPy is a general-purpose array processing package and a top-rated data science tool for beginners. It provides high-performance multidimensional array objects and tools for manipulating those arrays. This tool works with data as an N-dimensional array object. It provides tools for managing arrays and performing standard linear algebraic calculations such as array manipulation, basic statistics, and dot product operations.Numpy is a top machine machine learning skills.

2. Pandas

The Pandas library simplifies data manipulation and analysis in Python. Pandas works with two primary data structures. They are Series, a one-dimensional labeled array, and DataFrame, a two-dimensional labeled data structure. The Pandas package has many tools for reading data from various sources, including CSV files and relational databases.

Once the data is exposed as one of these data structures, pandas have a wide range of specific functions for cleaning, transforming, and analyzing the data. These include built-in missing data tools, simple plotting functions, and Excel-like pivot tables.

3. Matplotlib

Matplotlib is the most popular data science tool for beginners used for plotting. Many other popular plotting libraries depend on the matplotlib API, including the pandas plotting function and Seaborn.

Matplolib is a rich plotting library that includes functions for creating various graphs and visualizations. Additionally, it has features for creating animated and interactive charts.

4. Scikit-learn

Scikit-learn is an AI library, primarily written in Python and based on the SciPy library. It was initially developed as the Google Summer of Code project, where Google provided internships to students who had created significant open source software.cikit-learn is a top machine machine learning skill and it is highly required applied machine learning skill. Scikit-learn offers various strengths, including data clustering, regression, clustering, dimensionality reduction, model determination, and preprocessing.

5. TensorFlow

TensorFlow is a product of the Google Brain Team, which has come together to develop machine learning and is in too much demand among data scientists and machine learning engineers. It’s a software library for numerical computing and built for everyone from beginners to an expert. It allows one to access the power of deep learning without understanding some of its complicated principles. It is among the data science tools that help make deep knowledge accessible to thousands of companies.

6. Nltk

The Natural Language Toolkit (NLTK) mainly aims to build Python programs that work with human language data for applying statistical natural language processing (NLP).

nltk contains text processing libraries for tokenization, parsing, classification, stemming, tagging, and semantic reasoning. It also includes graphical demonstrations and sample data sets accompanied by a cookbook and a book that explains the principles behind the underlying language processing tasks that NLTK supports.

So, these are some important and top skills required for applied machine learning in 2022.