Introduction
Data analytics and machine learning go hand in glove. In order for you to become an established data scientist, it is essential to learn machine learning (ML) and generative AI as well. Did you know that when machine learning was first introduced, it was process intensive and demanded a specific set of tools and skilled resources to handle tasks? With cloud computing, organizations were able to implement machine learning easily as it was cost-efficient. They were able to make full use of machine learning in performing data analysis and this transformed many operations. Here are four advanced machine learning concepts that are important for data scientists.
Overview of Machine Learning in Data Analytics
Data scientists and machine learning engineers need to have a clear understanding of how ML algorithms play an important role in data analytics. These algorithms drive the analytics process by evaluating data, exploring, and discovering insights with the objective of making decisions that drive business outcomes. ML generates results that are purely driven by data and unbiased. It helps in understanding the trends which may not be found if the analysis is done manually. As a data scientist, you can rely on ML powered by cloud computing to create more scenarios that answer questions from various possible scenarios, making the output more credible and resourceful.
When integrated with analytics platforms, ML can help data scientists get data quickly and ensure faster decision-making. This is because ML can empower analytics platforms to analyze data sets quickly and generate contextual visualizations with suggestions. One such tool is AutoML, which helps boost productivity by automatically testing various ML algorithms to help the analyst find the most accurate model for their use case. However, if a data analyst does not depend on machine learning, the results would be based on professional experience and opinions which could be biased or contain errors.
ML algorithms enhance traditional analytics by identifying patterns, trends, and correlations that may go unnoticed by traditional analytics. They help data scientists make decisions without any bias and make visual recommendations on findings for more credible results. They achieve this by quickly populating dashboards with analytics-driven insights.
Basics of Machine Learning and Frameworks Used
As a data analyst, you can take up an advanced machine learning course that is sure to boost your career and open the door to many opportunities. However, before you learn advanced concepts, you need to understand the basics of machine learning and the frameworks used. As a subset of artificial intelligence, ML allows computers to learn and improve from experience without being programmed. ML algorithms analyze data, identify patterns, create a data model that can make predictions, and generate results based on the findings. Did you know that ML improves over time as it is exposed to more data?
Software developers, data scientists, and machine learning engineers benefit from using the machine learning framework tool. They can rely on this tool as it allows them to build ML models without diving into the underlying working principle of the machine learning algorithms. ML frameworks have similar working libraries that simplify the process of developing machine learning models. Here are some popular ML frameworks that are used by many tech giants and MNCs.
Commonly Used Machine Learning Frameworks
TensorFlow is a popular ML framework with a collection of pre-trained models that help deep neural scientists, and engineers to create deep learning algorithms and models. It clusters together ML and deep learning models and renders them through big datasets. This helps in training these models to analyze and create feasible models of their own. TensorFlow renders computational graph visualizations, provides seamless performance, and enables machines to extract discrete data, making it a nearly indispensable tool in the industry.
Did you know that MXNet is a robust framework that supports scalability and customization? It is known for its portability and can contain algorithms that need convolutional neural networks, and long and short term memory networks. MXNet is widely used in logistics, healthcare, and manufacturing. It is also useful among developers as it supports languages like Python, JavaScript, C++, R, Scala, and more.
Supervised and Unsupervised Learning
These are the two main types of machine learning that are widely followed in data analytics and data science. Supervised learning is where the machine is trained on a set of labelled data, where the input data is paired with the desired output. Labeled data consists of examples with the right answer. The machine is taught to predict the output for the new input data. It learns the relationship between inputs and outputs. For instance, the relationship between images of animals and animal names. Supervised learning is used for classification, regression, and object identification. In simple terms, data scientists use this learning method to classify data, predict outcomes based on inputs, and identify potential risks to mitigate them.
Regression is a type of supervised learning that predicts continuous values like house prices, customer churn, stock prices, and other similar aspects. Regression algorithms learn a function that maps the input features to the output value. A machine trained with supervised learning can make predictions on new, unlabeled data.
Unsupervised learning is a form of machine learning where the machine learns from unlabeled data, as in data with no labels or categories. The aim of unsupervised learning is for data scientists to discover relationships and patterns in data without any guidance. The machine’s objective is to group unsorted information according to patterns, differences, and similarities without any prior training of data. In unsupervised learning, the machine is restricted to find the hidden structure in unlabeled data by itself. Unsupervised learning is used to examine the gathered data and distinguish themselves between several groups. For instance, data scientists can gather data related to a demographic of people using smartphones and distinguish between iPhone users and Samsung users. This helps in categorizing mobile users without depending on pre-existing labels.
Advanced Regression Techniques
When it comes to supervised learning, regression learning is widely used across many industries. These techniques are used to predict probabilities and derive an appropriate output. Linear regression is a supervised machine learning algorithm that calculates the relationship between the dependent variable and one or more independent features using linear equations to observe data. For instance, the weight of a person is linearly related to their height, which shows a linear relationship. When the weight increases the height also increases.
Logistic regression is another crucial machine learning technique that uses a mathematical function to predict the possibility of a binary outcome. This regression method can be used to preprocess data. For instance, a large range of values like bank transactions can be sorted into a smaller and finite range of values using logistic regression. This method is used in spam detection, medical diagnosis, churn prediction, and loan default prediction.
Whether you are a fresher or a professional with a few years of experience under your belt, you can take up Eduinx’s data science with generative AI course to up skill yourself in your career. Eduinx’s trainers have over 10 years of experience in machine learning and data science. They offer a holistic learning experience that caters to your needs. You can get a comprehensive understanding of advanced machine learning, generative AI, and data visualization through this course. You can learn machine learning with data analytics by getting in touch with Eduinx.
Reference links:
https://www.geeksforgeeks.org/ml-machine-learning/ https://www.oracle.com/in/business-analytics/what-is-machine-learning-for-analytics/ https://www.projectpro.io/article/machine-learning-frameworks/509 https://www.geeksforgeeks.org/supervised-unsupervised-learning/
