What is Machine Learning and How to Become a Machine Learning Engineer

Nowadays, AI has become familiar in various fields and has found its place in human life. It is growing rapidly, and many people struggle to keep up with AI developments. Artificial Intelligence (AI) is a branch of machine learning, and it is ubiquitous in our lives, even though we may not recognize it clearly. You can see AI in your cell phone when you unlock it with facial recognition or when you use your car keylessly, among other applications. Researchers and developers are continuously working on developing machine learning algorithms to help people have better lives by making processes automated.

What is Machine Learning?

In simple terms, machine learning is a science that enables machines to learn from their experiences or given information and then react accordingly. For such algorithms, we first train the machine and then expect it to react. Sometimes, we provide instructions to the machine learning algorithm and expect it to learn and then react. Unlike humans who have brains and thinking abilities, machines need to be taught how to think and react. For example, if we teach a cleaner robot by using a machine learning algorithm, it can detect when a room is dirty and react by cleaning it. This process involves teaching the robot to recognize when the room is messy or covered with dust, using machine learning algorithms and sensors to gather data, and defining the criteria for when cleaning is needed.

Becoming a Machine Learning Engineer

To become a machine learning engineer, you need specific skills, primarily categorized into programming languages and the mathematics behind machine learning algorithms. While you don’t need to be an expert in developing machine learning algorithms for many jobs, understanding how to use and work with them is essential. Having a solid knowledge of machine learning algorithms helps you understand their applications better. It’s crucial to know how these algorithms are developed and why they are used. This knowledge allows you to select the appropriate machine learning algorithm for different datasets and perform parameter tuning. Not all machine learning algorithms work well for every dataset, so selecting the right one and tuning its parameters is essential.

To summarize, when selecting machine learning algorithms, we test each algorithm with different parameters. We choose the one that performs the best with its parameters. For instance, if we have ten ML algorithms to work with a dataset, we test each with various parameters and select the one that shows the best performance. This process involves model selection and parameter tuning. This article provides a comprehensive overview of machine learning algorithms and how to approach them. 

Machine Learning Engineering

Difference Between Data Mining and Machine Learning

Data mining was introduced in the 1930s, aiming to extract insights from data, uncover hidden patterns, and work with big data. Although both data mining and machine learning algorithms share similarities, they differ in technical implementations. Data mining techniques usually require human intervention to reveal information, whereas machine learning algorithms can be automated. ML algorithms only need human intervention during the training phase; once trained, they can operate without continuous human input, unless they require updates.

How is Machine Learning Classified?

Machine learning is generally classified into three categories:

  1. Supervised Learning: In supervised learning, the algorithm is provided with a set of labeled data, meaning that the data has been categorized or tagged with the correct answer. The algorithm then learns to identify patterns in the data and use those patterns to make predictions about new, unlabeled data.
  2. Unsupervised Learning: In unsupervised learning, the algorithm is not given any labeled data. Instead, it is given a set of unlabeled data and must find patterns or relationships within the data on its own. This type of learning is often used for tasks such as clustering, dimensionality reduction, and anomaly detection.
  3. Reinforcement Learning: In reinforcement learning, the algorithm learns by interacting with an environment. The algorithm takes actions in the environment and receives rewards or penalties based on those actions. The algorithm then tries to maximize its rewards by learning to choose the actions that are most likely to lead to positive outcomes.

machine learning types

Unsupervised Learning in Simple Words

Unsupervised learning refers to machine learning algorithms that do not require human-labeled data. Consider a clinical dataset of people with different attributes like gender and age. We can group them by these attributes, but if we want to discover hidden patterns, we use unsupervised learning to cluster individuals based on similarities. For example, patients of various ages and genders might be grouped together by unsupervised algorithms because they share other common traits. This process helps identify natural clusters, such as athletic individuals versus obese individuals, without predefined labels.

Supervised Machine Learning in Simple Words

Supervised machine learning algorithms work with labeled data to train models that can classify new data. Imagine a clinical dataset where patients are already labeled as muscular or obese. Using this labeled data, we train the algorithm to classify new patient information. When new data is provided, the model predicts whether the individual is muscular or obese. This process is called supervised learning because the model learns from pre-labeled data to make accurate predictions.

Reinforcement Learning in Simple Words

Reinforcement learning involves algorithms learning from their environment through rewards and penalties. Imagine a toddler exploring a room. If the toddler finds a cookie in a corner, they learn that searching corners often leads to finding cookies. If the toddler encounters a painful obstacle, they learn to avoid it. Reinforcement learning mimics this by teaching algorithms to maximize rewards and minimize penalties, continually improving their actions based on environmental feedback.

Difference Between Automation and Machine Learning

Automation and machine learning are distinct concepts. Automation follows predefined rules to perform repetitive tasks, yielding consistent results without learning. For example, an email service sending messages upon pressing “send” is automated. In contrast, machine learning involves training models with data to predict outcomes. If an email filter learns to identify spam through analysis and adjusts its behavior, this is machine learning. Automation does not adapt or learn, whereas machine learning continuously improves based on new data.

Difference Between Machine Learning and Deep Learning

Machine learning, a subset of artificial intelligence, involves algorithms that learn from data. Deep learning, a subset of machine learning, uses neural networks to analyze data. Think of deep learning as multiple linear regressions where the output of one regression becomes the input for the next, creating a layered, complex model. Deep learning is used for tasks like image and speech recognition, providing more powerful and accurate results compared to traditional machine learning techniques.

How to Become a Data Scientist or Machine Learning Engineer

Machine learning engineers and data scientists have overlapping yet distinct responsibilities. Machine learning engineers need to understand programming, the mathematics behind algorithms, and how to develop and deploy models. They also monitor and optimize these models. Data scientists focus on analyzing and manipulating data to derive insights. Although both roles require knowledge of machine learning algorithms, engineers need deeper technical skills. The following sections will list essential skills for aspiring machine learning engineers, which are also relevant for data scientists.

1. Mathematics and Statistics

  • Probability and Statistics
  • Linear Algebra
  • Calculus
  • Discrete Mathematics

2. Programming Languages

  • Python: Essential for data science and machine learning, with libraries such as NumPy, Pandas, Scikit-Learn, TensorFlow, and PyTorch.
  • R: Important for statistical analysis and data visualization.
  • SQL: For database querying and data manipulation.
  • Java/Scala: Often used in big data frameworks like Hadoop and Spark.

3. Data Manipulation and Analysis

  • Data Cleaning and Preprocessing
  • Data Wrangling
  • Exploratory Data Analysis (EDA)
  • Feature Engineering

4. Machine Learning

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Deep Learning (Neural Networks, CNNs, RNNs)
  • Model Evaluation and Validation
  • Hyperparameter Tuning
  • Ensemble Methods

5. Big Data Technologies

  • Hadoop
  • Spark
  • Hive
  • Kafka

6. Data Visualization

  • Matplotlib
  • Seaborn
  • Plotly
  • ggplot2 (for R)
  • Tableau or Power BI

7. Software Engineering Practices

  • Version Control (Git)
  • Software Development Life Cycle (SDLC)
  • Testing and Debugging
  • Code Optimization

8. Cloud Computing

  • AWS (Amazon Web Services)
  • Google Cloud Platform (GCP)
  • Microsoft Azure

9. Data Engineering

  • ETL (Extract, Transform, Load) Processes
  • Database Management Systems (SQL and NoSQL)
  • Data Pipeline Creation and Management

In the list of skills for data scientists or machine learning engineers, you’ll notice some are related to data engineering or software development. Although your primary role isn’t that of a software developer or data engineer, knowing these skills can significantly increase your job prospects and earning potential. It’s beneficial to familiarize yourself with these areas as it allows you to work effectively with software developers and other engineers on your team. This knowledge can also position you for leadership or management roles in your career.

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