Machine Learning (ML) is a domain of Artificial Intelligence (AI) that involves developing algorithms to learn from and make predictions based on data. Applied machine learning refers to applying machine learning algorithms to solve real-world problems, such as image classification, speech recognition, and natural language processing. Hence, applied machine learning in Python becomes imperative to multiple organizational tasks.
What is applied machine learning in Python?
Applied machine learning refers to using machine learning techniques and algorithms to solve real-world problems and make predictions or decisions based on data. It involves taking theoretical concepts from machine learning and implementing them in practical applications to address specific challenges or tasks.
The machine learning life cycle is the process of building, training, and deploying machine learning models. It consists of several steps that help ensure the model is accurate and meets the requirements. These are the critical steps of the machine learning life cycle:
Problem definition: The problem to be solved is clearly defined in this step. This includes defining the target audience, understanding the available data, and determining the goals and objectives of the model.
Data collection and preparation: In this step, the data that will be used to train the model is collected and prepared for analysis. This includes cleaning and preprocessing the data, removing any missing or corrupted data, and transforming it into a format that the ML algorithms can process.
Exploratory Data Analysis (EDA): In this step, the data is analyzed to gain insights and identify trends or patterns. This step is critical in understanding the data and deciding which machine learning algorithms best suit the problem.
Model selection: In this step, the appropriate machine learning algorithms are selected based on the nature of the problem, the data, and the results of the EDA.Model training: In this step, the machine learning algorithms are trained on the data to develop the model. The model is then evaluated to ensure it is performing as expected.
Model tuning and improvement: In this step, the model is fine-tuned to improve its accuracy and performance. This may involve adjusting the algorithms, adding new features, or adjusting the parameters.
Deployment: In this step, the model is deployed and made available to end-users. This includes integrating the model into the existing systems and processes and ensuring it works as expected.
Monitoring and maintenance: In this step, the model is monitored and maintained to ensure it performs as expected. This includes updating the model as new data becomes available, addressing any issues, and making necessary improvements.
The machine learning life cycle is an iterative process, and each step is critical in developing a high-performing machine learning model. It requires collaboration between data scientists, engineers, and stakeholders to ensure the model is accurate and meets the needs of the end users.
Here’s how beginners can learn different aspects of a machine learning cycle:
Python
Python is a user-friendly general-purpose language with wide community support and availability with a vast library of machine learning frameworks. It could be a preferable choice for beginners to learn Python.
Here are some ways Python helps in each of these stages:
Creating ML models: Python has libraries like TensorFlow, Keras, and PyTorch that provide a high-level interface for building and training machine learning models. These libraries allow developers to define and configure models and perform different tasks like gradient descent and backpropagation, making it easier to create complex models.
Training ML models: Python provides a variety of libraries for preprocessing and transforming data, such as Pandas and NumPy. These libraries allow developers to load, manipulate, and clean data, making it easier to prepare data for training. Additionally, Python libraries like Scikit-learn provide a range of algorithms and tools for training machine learning models, including regression, classification, clustering, and dimensionality reduction algorithms.
Testing ML models: Python provides libraries like Scikit-learn, which includes tools for evaluating the performance of machine learning models, such as accuracy, precision, recall, and F1 score. Additionally, Python has visualization libraries like Matplotlib and Seaborn, which can help developers visualize model performance and identify areas for improvement.
Excel for data collection
Excel is a popular tool for data collection and preparation for machine learning, as it provides many features and tools for working with data. As a beginner, it is one of the first skills to learn to work with data. Here are some ways Excel helps in data collection and preparation:
Data Collection: Excel provides a flexible interface for collecting data from various sources, such as spreadsheets, databases, and text files. With Excel, you can easily import, clean, and transform data to prepare it for analysis.
Data Cleaning: Excel provides various tools for cleaning and transforming data, including functions for removing duplicates, replacing missing values, and transforming data into a consistent format. These tools make it easier to prepare data for analysis and machine learning.
Data Visualization: Excel provides a variety of charts and visualizations, including bar charts, line charts, and scatter plots, that can help you understand patterns in your data. These visualizations can help you identify trends, outliers, and other important features in your data, which can be useful for feature selection and model building.
Data Analysis: Excel provides a range of tools for data analysis, such as pivot tables, regression analysis, and statistical functions. These tools can help you analyze exploratory data, identify relationships between variables, and prepare data for machine learning.
Tableau and Power BI for data visualization
To become a data scientist, professional learning data visualization becomes vital to create value for decision-makers. Tableau and Power BI are popular data visualization tools that help create interactive, visually appealing dashboards and reports. Here’s how they help:
Tableau: Tableau provides a drag-and-drop interface for creating interactive visualizations, making it easy to explore and analyze data. Tableau also offers a wide range of pre-built templates and visualizations that can be customized to fit specific business requirements.
Power BI: Power BI provides comprehensive data visualization and analytics tools, including interactive dashboards, reports, and visualizations. Power BI also offers a wide range of data sources and connectors, making it easy to connect and analyze data from various sources.
Tableau and Power BI allow users to share visualizations and dashboards with stakeholders, enabling data-driven decision-making. They also provide real-time updates and collaboration features, making collaborating and communicating insights with others easy.
Conclusion
A well-structured applied machine learning course can provide the foundation for beginners to learn and excel in machine learning. The course offers hands-on experience with practical applications and the opportunity to learn from industry experts. It’s a great investment for anyone looking to start a career in this field.
