#11 Machine Learning 101 for Data Analysts

📊 Machine Learning 101 for Data Analysts

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For a brief on pre-ML, check out the concept on Time Series in my previous article “Perform a Time Series Correctly”. We will be covering more ML topics for data analysts in the next few months. Each newsletter representing a topic to help you as a data analyst learn more about ML.

Now onto some fun stuff.

Why ML is Relevant to Data Analysts:

As the world leans more into AI and ML tools, data analysts equipped with ML skills can offer more sophisticated insights and add substantial value to their organizations. Few key areas where ML can be integrated into the daily life if you’re a data analyst:

• Predictive Analytics: Utilize ML models to forecast trends, behaviors, and outcomes, enhancing strategic decision-making.

• Automated Data Processing: Implement ML algorithms to streamline data cleaning and processing, boosting efficiency and accuracy.

• Anomaly Detection: Leverage ML to spot outliers and anomalies, crucial for sectors like finance and healthcare.

• Customer Segmentation: Apply clustering techniques to segment customers more accurately, improving marketing and service.

Types of Machine Learning

ML can be broadly classified into three main types, each with distinct methodologies and applications. Understanding these types will help you in selecting the right approach for different data analysis tasks.

• Supervised Learning: This type involves training a model on a labeled dataset, where the correct output is known. The goal is to learn a pattern that maps inputs to outputs. Example: Predicting house prices based on features like size and location using linear regression.

• Unsupervised Learning: In unsupervised learning, the data has no labels, and the model tries to learn the underlying structure from the data itself. Example: Segmenting customers into different groups (clustering) based on purchasing behavior without prior knowledge of the groupings.

• Reinforcement Learning: This type involves learning to make sequences of decisions by receiving rewards or penalties. Example: Training a robot to navigate a maze where it learns by trying different paths and getting feedback via rewards when it reaches the goal.

Commonly Used Algorithms

Several foundational algorithms are essential for anyone starting in machine learning, each suited for different types of data and problems.

• Linear Regression: Used for predicting a continuous value. Example: Estimating the selling price of a car based on its features like age, mileage, and brand.

• Decision Trees: A model that uses a tree-like graph of decisions and their possible consequences. Example: Deciding on loan approval based on criteria like income, credit score, and employment history.

• k-Nearest Neighbors (k-NN): A simple algorithm that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions). Example: Recommending products based on the purchase history of similar customers.

• Clustering (e.g., k-Means): A method of grouping a set of objects in such a way that objects in the same group (cluster) are more similar to each other than to those in other groups. Example: Organizing articles into topics based on their content.

I listed a few courses in the next section that anyone could start with to understand Machine Learning a bit more deeply. I am not affiliated with ant of these courses, neither do they sponsor me but I can vouch for their content.

 📰 Data Tools, Articles and Resources 

1. Kaggle: Intro to Machine Learning Module

2. Coursera: Machine Learning Specialization

3. Scikit-learn: A Python library that provides simple and efficient tools for data mining and data analysis. It's built on NumPy, SciPy, and matplotlib.

4. TensorFlow: An end-to-end open-source platform for machine learning designed by Google. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML.

5. PyTorch: An open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab.

6. Pandas: An open-source data analysis and manipulation tool, built on top of the Python programming language. It offers data structures and operations for manipulating numerical tables and time series.