material links of unit 1&2&3:
Lab manual link:
Machine Learning-LAB-R20-3-1
Well, let's start with a brief introduction to artificial intelligence and machine learning.
Artificial intelligence is a branch of computer science in which we can create intelligent machines that can behave like humans, think like humans, and make decisions. Actually In 1951, the first AI program was introduced by Christopher, and AI was introduced by John McCarthy in 1955.
Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed.It was introduced by Arthur Samuel in 1959. It enhances systems to learn from data, improve performance from experience, and predict new data without human interference. Machine learning is a subset of AI.
Deep learning is a collection of statistical techniques for machine learning feature hierarchies that are actually based on artificial neural networks. I was introduced by Dina Dechter in 1986. It is subset of Machine learning
Before going into our main concept of machine learning, see various areas that use machine learning; they are image recognition, virtual personal assistants, automatic translation, traffic prediction, web search and recommendation engines, online fraud detection, medical diagnosis, text and speech recognition, email spam filtering, etc.
Tip: If you know this 7-step process in machine learning, it's clear that you can build any project in machine learning in an efficient manner.
- Define the problem. [Understand the problem first.]
- Data Gathering
- Data preprocessing [noisy data removal, data splitting]
- Choose model
- Train the model.
- Test the model.
- Deployment or prediction
Types of machine learning:
- Whether or not they are trained under human supervision (SUPERVISED, UNSUPERVISED, SEMI-SUPERVISED, and REINFORCEMENT LEARNING).
- Whether or not they can learn incrementally on the fly (Online Learning vs Batch Learning)
- Whether they work by simply comparing new data points to known data points or instead detect patterns in the training data and build a predictive model [instance-based learning versus model-based learning]
Most of the time, we discuss the human supervision type of machine learning.
- Supervised learning
- Unsupervised learning
- semi-supervised learning
- Reinforcement learning
Supervised learning: we train the machines using the labeled dataset, and based on the training, the machine predicts the output. In this, we feed training data to an algorithm that includes the desired solutions in it. It has two methods to solve problems, which are:
- Classification: it was used when output data is categorical data.
- Regression: it was used for predicting or estimating a continuous or quantitative output value.
e.g., image recognition, spam detection
Unsupervised learning: In this model, the model, the models are trained using an unlabeled dataset and predict new data without any supervision, so we say it is unsupervised. It was easier as compared to a labeled dataset. It has two methods:
- Clustering: method of grouping data items that are similar into a single cluster
- Association: It is used for finding the relationships between variables in large data sets.
Semi-supervised learning lies in between supervised and unsupervised learning. To overcome the drawbacks of both, it came into existence. Its training data is a combination of both labeled and unlabeled data. It has both the features of supervised and unsupervised learning.
Reinforcement learning: we simply say it as feed-back learning. It follows the trail-and-error method to get the desired solution.
Loss Functions:
Loss is a number indicating how bad the model's prediction was on the sample
Training loss: it is the error of the model on the training set.
Testing loss: it is the error of the model on the testing set.
A loss function measures the error between the predicted and actual values in a machine learning model.
There are different loss functions, such as
- Mean Squared Error (MSE)
- Cross-entropy loss
- Mean Absolute Error (MAE)
- Huber loss
- Hinge loss
- Quantile loss
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