In Week 3, we delved into machine learning implementations, focusing on when and why to use machine learning, as well as when not to use it. We explored the significance of having sufficient and meaningful data, along with the need for a well-defined task or hypothesis. Moreover, we examined the role of various Python libraries in the implementation process and how they contribute to different stages of a machine learning project.

In this lesson, we’ll shift our attention shifts to understanding how machine learning is done in the real world and the concept of ML pipelines. We will discuss practical applications of machine learning across various industries and the challenges that practitioners face. Additionally, we’ll explore ML pipelines, which streamline the process of developing, deploying, and maintaining machine learning models, ensuring efficiency and scalability in real-world scenarios. Join us as we dive deeper into the practical aspects of machine learning and its implications on modern-day problem-solving.

ML Pipelines

Machine learning pipelines are structured sequences of steps, encompassing the entire process from design and data acquisition to model selection, evaluation, and deployment into production. As discussed in previous lessons, each step in the pipeline is typically executed using pre-written libraries, streamlining the development process.

In today’s rapidly evolving landscape, models are rarely built from scratch, except in research settings. Many machine learning tasks, such as spam detection, digit recognition, and speech recognition, are now considered “solved problems,” with readily available solutions that can be easily integrated into ML pipelines, making them more efficient and effective in addressing real-world challenges.

Neural Networks – The Most Iconic Models

Taking a cue from neurology, neural networks in machine learning aim to replicate natural learning on computers. This requires understanding how various living creatures’ brains work, not just humans. Inspired by the astonishing complexity of brain structure and function, neural network models strive to imitate these aspects in their design and action. While current neural networks may fall short compared to actual brains, they’ve shown incredible proficiency in handling a wide range of tasks. This success has cemented their position as some of the most iconic and popular models in the world of machine learning.

How Neural Networks Work

Neural networks process data in a forward manner, taking inputs and passing them through interconnected layers to produce a prediction or output. The neurons within one layer are connected to all neurons in the preceding and succeeding layers. To move the data forward, neural networks employ basic arithmetic operations, such as addition and multiplication. A single-neuron neural network, although simple, can help demonstrate the core principles behind these powerful models and their ability to make accurate predictions from a given set of data.

Understanding Weights, Biases, and Activation Functions

A single-layer neural network uses inputs (X), learnable weights (W), and biases (B) to represent a specific function. The output, y, is calculated as y = f(W * X + b), where f is the activation function responsible for “activating” the input provided to the next layer. In upcoming lessons, we will delve into deep learning, which involves neural networks with more than one neuronal layer, also known as hidden layers. These hidden layers are distinct from the input and output layers. The ultimate goal in machine learning is to find the optimal weights (W) and biases (B) to efficiently tackle the task at hand!

Modeling the OR function with an NN

Addressing Problems & Limitations of the Simple Model

While we’ve explored a single-neuron neural network with a linear output space, it’s important to acknowledge its limitations. In particular, this simple model struggles when it comes to modeling non-linear regions. To overcome this challenge, we’ll need to delve into more advanced concepts and techniques, which we’ll discuss in future lessons. By enhancing our understanding of neural networks, we can better address the complexities of real-world problems and design more effective models.

Linear Output Space
Non-Linear Regions

Challenges of Single-Layer Neural Networks: The XOR Problem

A significant issue with single-layer neural networks is their inability to handle problems with non-linearly separable input/output spaces, such as the XOR problem. In this case, it’s impossible to separate black and white dots using a single line. To address these challenges, more advanced neural network architectures are needed. By exploring deeper and more complex models, we can better tackle intricate problems and design more effective solutions for non-linear scenarios.

XOR Problem

Model Capacity and Design: Hidden Layers and Neurons

In the process of designing a machine learning pipeline, one crucial aspect to consider is the complexity of our model. This complexity can be influenced by factors such as the number of hidden layers and the number of neurons per layer. By increasing the number of neurons and layers in our neural network, we can tackle more complex problems like the XOR problem, which cannot be solved using a single neuron. Thus, understanding and adjusting model capacity plays a vital role in achieving more accurate and sophisticated results in machine learning projects.


In conclusion, this week’s lesson provided valuable insights into the real-world applications of machine learning and the importance of understanding ML pipelines. We explored the inner workings of neural networks, their capabilities and limitations, and how adding layers and neurons can help tackle more complex problems. Furthermore, we discussed the significance of model capacity and design in achieving accurate and sophisticated results in machine learning projects.

As we move forward to next week’s lesson, we will dive into a practical example of a machine learning project. This hands-on approach will allow you to apply the concepts and techniques learned so far, offering a deeper understanding of how machine learning can be used to solve real-world problems and challenges. Stay tuned for an exciting journey into the practical side of machine learning!

You can access this in code by clicking here for the github code.