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Each tutorial builds upon concepts from previous posts while introducing new techniques and best practices. Whether you're new to machine learning or looking to expand your skills, this series provides hands-on experience with real-world datasets and modern ML tools.
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Have you ever wondered how to get started with machine learning? This series of posts will guide you through practical implementations using two of Python's most popular frameworks: Scikit-learn and TensorFlow. Whether you're a beginner looking to understand the basics or an experienced developer wanting to refresh your knowledge, we'll progress from basic classification tasks to more advanced regression problems.
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The series consists of four parts:
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1.**[Getting Started with Classification using Scikit-learn](../blog/2023/01_scikit_simple)** (You are here)<br>Introduction to machine learning basics using the MNIST dataset
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2.**[Basic Neural Networks with TensorFlow](../blog/2023/02_tensorflow_simple)** (Part 2)<br>Building your first neural network for image classification
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3.**[Advanced Machine Learning with Scikit-learn](../blog/2023/03_scikit_advanced)** (Part 3)<br>Exploring complex regression problems and model optimization
1.**[Getting Started with Classification using Scikit-learn]({{ site.baseurl }}/blog/2023/01_scikit_simple)** (You are here)<br>Introduction to machine learning basics using the MNIST dataset
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2.**[Basic Neural Networks with TensorFlow]({{ site.baseurl }}/blog/2023/02_tensorflow_simple)** (Part 2)<br>Building your first neural network for image classification
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3.**[Advanced Machine Learning with Scikit-learn]({{ site.baseurl }}/blog/2023/03_scikit_advanced)** (Part 3)<br>Exploring complex regression problems and model optimization
Figure 7: Most challenging digits for the model to predict, showing examples where the model has lowest prediction confidence.
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In Part 2, we'll explore how neural networks approach the same problem using TensorFlow, introducing deep learning concepts and comparing the two approaches.
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[Continue to Part 2 →](../blog/2023/02_tensorflow_simple)
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[Continue to Part 2 →]({{ site.baseurl }}/blog/2023/02_tensorflow_simple)
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In this second part of our machine learning series, we'll implement the same MNIST classification task using [TensorFlow](https://www.tensorflow.org/). While Scikit-learn excels at classical machine learning, TensorFlow shines when building neural networks. We'll see how deep learning approaches differ from traditional methods and learn the basic concepts of neural network architecture.
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The complete code for this tutorial can be found in the [02_tensorflow_simple.py]({{ site.baseurl }}/scripts/02_tensorflow_simple.py) script.
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### Why Neural Networks?
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While our Scikit-learn models performed well in Part 1, neural networks offer several key advantages for image classification:
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Figure 1: Training metrics over time showing model loss (left) and Mean Absolute Error (right) for both training and validation sets. The logarithmic scale helps visualize improvement across different magnitudes.
In this third part of our series, we'll explore more sophisticated machine learning techniques using [Scikit-learn](https://scikit-learn.org/stable/). While Parts 1 and 2 focused on classification, we'll now tackle regression problems and learn how to build complex preprocessing pipelines. We'll use the California Housing dataset to demonstrate these concepts.
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The complete code for this tutorial can be found in the [03_scikit_advanced.py]({{ site.baseurl }}/scripts/03_scikit_advanced.py) script.
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**Note**: The purpose of this post is to highlight the flexibility and capabilities of scikit-learn's advanced features. Therefore, this tutorial focuses on introducing you to those advanced routines rather than creating the optimal regression model.
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