Overview
This week we’ll embark on an exciting journey into the realm of machine learning theory, delving into the key technologies that power this transformative field. Our session will explore the fundamental concepts and principles underlying machine learning, offering a comprehensive understanding of various techniques and algorithms. As we bridge the gap between theory and real-world applications, associates will be better equipped to harness the potential of machine learning for client projects and drive innovation in an increasingly data-driven world.
What is Machine Learning?
Machine learning is the art of teaching computers to learn and make decisions just like humans do, but by using data and patterns instead of explicit instructions. It’s a part of artificial intelligence that helps machines get smarter over time, allowing them to tackle tasks and solve problems more effectively. This incredible technology has become an essential tool in many fields, transforming the way we work, live, and interact with the world around us.
Key Points:
- Machine learning is teaching computers to learn and make decisions without being explicitly taught or instructed
- Learn from an experience E to perform a task T as measured by performance P
Why Use Machine Learning?
Machine learning empowers us to leverage data for predicting future trends with accuracy. By identifying patterns in historical data, machine learning algorithms can make informed decisions on new data points. This helps organizations streamline processes, enhance customer experiences, and drive innovation in our fast-paced, ever-evolving world.
Key Points:
- Learning requires data, and ML is used to make predictions on data that has not yet been seen, with a margin of error
What is Learning & How is it Done?
- Features and labels: any task that can be formalized as {input X : output Y} can be done using ML, and with enough data and a certain model, the relationship y = f(x) can be learned
- For Ex. Speech recognition: you can hard-code an algorithm to recognize spoken words, but you could also use an ML algorithm that takes example recordings for each word, along with labels
- Bottom line: learning is all about patterns! The machine “learns” relationships from examples
Types of Learning:
Supervised
Supervised learning involves training algorithms using labeled data, where the correct answers are already known. This process allows the algorithm to learn the underlying patterns, enabling it to make accurate predictions when presented with new, unseen data.
Unsupervised
Unsupervised machine learning involves algorithms that learn from unlabeled data, seeking to identify hidden structures and patterns. With no provided labels, the model clusters or groups the data based on similarity, unveiling valuable insights and revealing underlying relationships within the dataset.
Semi Supervised
Semi-supervised machine learning combines the approaches of both supervised and unsupervised learning, utilizing a mix of labeled and unlabeled data. This method leverages the known labels to guide the algorithm while also discovering hidden patterns, resulting in improved accuracy and efficiency in tasks such as classification and prediction.
Within each of these types:
- Batch Learning: also known as offline learning, is done with all data in memory before deployment (image recognition, etc)
- Online Learning: data instances come in ‘on the fly’ and train the model dynamically (time series data, etc)
Common Machine Learning Problems
Regression
Using features (I.e variables X) to predict a numerical outcome (I.e a double)
Classification
Using features (I.e variables X) to predict a categorical outcome (I.e a string denoting class membership, like ‘spam’ and ‘not spam’
We’ll cover more in next weeks lesson
Challenges of ML
- Lack of data
- Quality and quantity oftentimes a problem
- ‘Curse of Dimensionality’
- Lots of data means lots of parameters to learn!
- Problem: the more columns in our table, the higher the dimensions, and the less we can capture the data with our model.
- Overfitting
- Model can memorize training data and perform poorly on new data (generalization)
Conclusion
As we conclude this week’s introduction to machine learning, we have laid the groundwork for understanding the fundamentals of this transformative field. We’ve explored various types of learning, key concepts, and the challenges faced in machine learning.
Next week, we’ll dive deeper into common machine learning problems and troubleshooting techniques, equipping you with essential skills to tackle real-world challenges and continue your journey towards becoming proficient in this ever-evolving domain.
