Data Science News
Discover the latest Data Science development news, repositories, and conferences at Hackertab.
Latest Data Science articles
Best Alternative to pprint: Introducing SetPrint for Better Python Data Formatting
Issues with Existing Formatting Tools When working with data structures like lists or...
19 May 2025devto
![📅 Day 1/75 of LeetCode Practice – [Today’s Focus: Arrays / Strings / Sliding Window]::PART 2](/_next/image?url=https%3A%2F%2Fmedia2.dev.to%2Fdynamic%2Fimage%2Fwidth%3D1000%2Cheight%3D500%2Cfit%3Dcover%2Cgravity%3Dauto%2Cformat%3Dauto%2Fhttps%253A%252F%252Fdev-to-uploads.s3.amazonaws.com%252Fuploads%252Farticles%252F2rimu661eq5ks2woydyr.png&w=640&q=75)
📅 Day 1/75 of LeetCode Practice – [Today’s Focus: Arrays / Strings / Sliding Window]::PART 2
📈 Best Time to Buy and Sell Stock – (LeetCode #121) In this post, I’ll walk through one of the most...
19 May 2025devto
Why iGaming Platforms Are Becoming the Most Interesting Tech Startups in 2025
The iGaming industry is no longer just about flashy slot games and online poker tables. It’s becoming...
19 May 2025devto
From Coder to Creator: Your First Steps in the World of AI Development
So, you're curious about Artificial Intelligence (AI) and want to build amazing things with it? That's fantastic! The world of AI is booming, and the possibilities are truly endless. This guide will give you a practical roadmap to start your AI development journey, even if you're just starting out with code. We'll break down the essential steps, explain key concepts, and provide hands-on examples to get you building. Why AI Development? AI is no longer a futuristic fantasy. It's powering everything from your social media feeds to self-driving cars. As an AI developer, you'll be able to: Solve Real-World Problems: Create solutions for healthcare, finance, education, and countless other industries. Build Innovative Applications: Design cutting-edge applications that were previously unimaginable. Be in High Demand: AI skills are highly sought after, making it a rewarding and lucrative career path. Step 1: Mastering the Fundamentals Before diving into complex AI models, you need a solid foundation in programming. Choose a Language: Python is the most popular language for AI development due to its extensive libraries and easy-to-read syntax. Other options include R, Java, and C++. We'll focus on Python in our examples. Learn Basic Programming Concepts: Understand variables, data types (integers, strings, booleans), control flow (if/else statements, loops), and functions. Example (Python): # A simple program to check if a number is even or odd number = 10 if number % 2 == 0: print(f"{number} is even") else: print(f"{number} is odd") # A function to add two numbers def add_numbers(x, y): """This function adds two numbers and returns the sum.""" return x + y result = add_numbers(5, 3) print(f"The sum of 5 and 3 is: {result}") # A loop to print numbers from 1 to 5 for i in range(1, 6): print(i) Technical Deep Dive: Indentation in Python Python uses indentation (spaces or tabs) to define code blocks. This is crucial for readability and how the interpreter understands your code. Incorrect indentation will lead to errors. Be consistent with your indentation – typically, 4 spaces are used per level. Step 2: Exploring Data Science Essentials AI thrives on data. Understanding data science concepts is critical. Data Structures: Learn about lists, dictionaries, and arrays (using NumPy). Data Manipulation: Explore how to clean, transform, and analyze data using libraries like Pandas. Data Visualization: Master creating charts and graphs to understand data patterns using libraries like Matplotlib and Seaborn. Example (Python with Pandas): import pandas as pd # Create a sample DataFrame data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'], 'Age': [25, 30, 28, 22], 'City': ['New York', 'London', 'Paris', 'Tokyo']} df = pd.DataFrame(data) # Print the DataFrame print(df) # Get the average age average_age = df['Age'].mean() print(f"\nAverage Age: {average_age}") # Filter the DataFrame to show only people older than 25 older_than_25 = df[df['Age'] > 25] print(f"\nPeople older than 25:\n{older_than_25}") Technical Deep Dive: Pandas DataFrames Pandas DataFrames are powerful data structures that resemble spreadsheets or SQL tables. They allow you to easily store, manipulate, and analyze tabular data. Understanding how to create, filter, group, and aggregate data in DataFrames is fundamental to working with data in AI projects. Step 3: Diving into Machine Learning Now for the exciting part! Machine learning (ML) is a core component of AI. Understand ML Concepts: Learn about supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), and reinforcement learning. Explore ML Algorithms: Get familiar with algorithms like linear regression, logistic regression, decision trees, support vector machines (SVMs), and k-means clustering. Use ML Libraries: Scikit-learn is your best friend here. It provides a wide range of tools for building and evaluating ML models. Example (Python with Scikit-learn): from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score # Sample data (replace with your own dataset) X = [[1, 2], [2, 3], [3, 1], [4, 3], [5, 5], [6, 4]] # Features y = [0, 0, 0, 1, 1, 1] # Labels (0 or 1) # Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # Create a Logistic Regression model model = LogisticRegression() # Train the model model.fit(X_train, y_train) # Make predictions on the test set y_pred = model.predict(X_test) # Evaluate the model accuracy = accuracy_score(y_test, y_pred) print(f"Accuracy: {accuracy}") Technical Deep Dive: Train/Test Split Splitting your data into training and testing sets is crucial for evaluating the performance of your ML model. The training set is used to train the model, while the testing set is used to assess how well the model generalizes to unseen data. A good split ensures that your model isn't simply memorizing the training data (overfitting). Step 4: Exploring Deep Learning (Optional, but Recommended) Deep learning (DL) is a subset of ML that uses artificial neural networks with multiple layers to analyze data. Learn about Neural Networks: Understand the basic structure of neural networks, including layers, neurons, activation functions, and backpropagation. Explore Deep Learning Frameworks: TensorFlow and PyTorch are the most popular DL frameworks. Build Simple Neural Networks: Start with basic models for image classification or natural language processing (NLP). Example (Python with TensorFlow/Keras): import tensorflow as tf from tensorflow import keras # Define a simple sequential model model = keras.Sequential([ keras.layers.Dense(128, activation='relu', input_shape=(784,)), # Input layer with 784 features (e.g., flattened image) keras.layers.Dense(10, activation='softmax') # Output layer with 10 classes (e.g., digits 0-9) ]) # Compile the model model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # Load the MNIST dataset (handwritten digits) (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() # Preprocess the data x_train = x_train.reshape(60000, 784).astype('float32') / 255 x_test = x_test.reshape(10000, 784).astype('float32') / 255 y_train = keras.utils.to_categorical(y_train, num_classes=10) y_test = keras.utils.to_categorical(y_test, num_classes=10) # Train the model model.fit(x_train, y_train, epochs=2, batch_size=32) # Train for 2 epochs # Evaluate the model loss, accuracy = model.evaluate(x_test, y_test) print(f"Accuracy: {accuracy}") Technical Deep Dive: Activation Functions Activation functions introduce non-linearity into neural networks, allowing them to learn complex patterns. Common activation functions include ReLU (Rectified Linear Unit), sigmoid, and tanh. The choice of activation function can significantly impact the performance of your model. Practical Implications: Where to Go Next Once you have a grasp of these fundamental concepts, you can start applying your knowledge to real-world projects. Start Small: Begin with simple projects like image classification or sentiment analysis. Find Datasets: Explore datasets on platforms like Kaggle or UCI Machine Learning Repository. Contribute to Open Source: Collaborate with other developers on open-source AI projects. Keep Learning: The field of AI is constantly evolving, so stay up-to-date with the latest research and technologies. Conclusion: Your Journey Begins Now Becoming an AI developer is a challenging but rewarding journey. By mastering the fundamentals, exploring data science and machine learning, and continuously learning, you can unlock the power of AI and build amazing applications. Don't be afraid to experiment, make mistakes, and learn from them. The world of AI is waiting for you! Inspired by an article from https://github.blog/ai-and-ml/vibe-coding-your-roadmap-to-becoming-an-ai-developer/ ...
19 May 2025hashnode
Data Science Foundations: Practical Approaches with the 80/20 Rule – Part 2
In Part 1, we focused on the early stages of a data science project—setting clear goals, planning for...
18 May 2025devto
Machine Learning for beginners
Hello there and welcome again to yet another post on machine learning for beginners. Today I will...
18 May 2025devto
💻What is Data Science? A Complete Beginner’s Guide to Projects, Machine Learning, and the Real Flow
If you're a student, fresher, or someone curious about this buzzing field called Data Science, you're...
17 May 2025devto
Machine Learning Tutorial: Your First Guide to AI Concepts
Machine learning (ML) has become one of the most transformative technologies of the 21st century,...
17 May 2025devto
Understanding the Basics of Machine Learning with Python
Understanding the Basics of Machine Learning with Python Machine learning (ML) is revolutionizing...
17 May 2025devto
How to Transform Jupyter Notebook into a Maintainable AI Project
A Python package named modinit that facilitates the rapid scaffolding of AI model training...
16 May 2025devto