This blog covers the basic knowledge needed for machine learning. You will learn about the different types of machine learning, the algorithms used, and the various applications.
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Machine learning is a subset of artificial intelligence in the field of computer science that deals with the construction and study of systems that can learn from data. It is seen as a way to make computers smarter and more capable of understanding the world.
Machine learning is based on the idea that computers can learn from data, without being explicitly programmed. This is different from traditional approaches to programming, where algorithms are designed by people. In machine learning, algorithms are designed by machines, using a set of training data.
The aim of machine learning is to build systems that are capable of automatically improving with experience. This is different from traditional systems, which are designed to perform specific tasks, and do not get better at those tasks unless specifically programmed to do so.
There are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning is where the machine is given a set of training data, and told what the correct outputs should be. The machine then uses this training data to learn how to map inputs to outputs. Once the machine has learned this mapping, it can be applied to new inputs to give correct outputs.
Unsupervised learning is where the machine is given a set of data but not told what the outputs should be. The machine has to learn for itself what features are important, and how inputs should be mapped to outputs. This can be used for tasks such as clustering, where the machine groups together similar examples.
Reinforcement learning is where the machine learns by trial and error through interactions with its environment. The machine receives positive reinforcement when it takes actions that lead it towards its goal, and negative reinforcement when it takes actions that lead it away from its goal.
Data pre-processing is a data mining technique that involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or biased. Data pre-processing is an important step in data mining because It allows you to transform your data into a format that will be more easily understood by the machine learning algorithms you use.
Data pre-processing includes cleaning, imputation, feature selection, and normalization.
Cleaning: Cleaning refers to the process of identifying and correcting errors in the data. This step is important because incorrect data can lead to inaccurate results.
Imputation: Imputation is the process of filling in missing values. This can be done by replacing missing values with the mean or median of the rest of the values in the column.
Feature Selection: Feature selection is the process of choosing which features (variables) to include in your model. This step is important because it can help improve the accuracy of your model and reduce the computational cost of training your model.
Normalization: Normalization is the process of rescaling your data so that all variables are on the same scale. This step is important because some machine learning algorithms require that all variables are on the same scale in order to work properly.
Before building predictive models, it is always a good idea to explore the data set that you are working with. This process is known as “data visualization”, and it is a key step in any machine learning project. There are many different ways to visualize data, but some of the most popular methods include scatter plots, bar charts, and histograms.
Scatter plots are used to show relationships between two variables. For example, you could use a scatter plot to show how age and height are related. Bar charts are used to compare different values. For example, you could use a bar chart to compare the average heights of different age groups. Histograms are used to show the distribution of a single variable. For example, you could use a histogram to show how many people in a room are taller than 6 feet tall.
Data visualization is an important tool for understanding your data set and identifying patterns that could be useful for predictive modeling. If you’re new to data visualization, we recommend that you check out some of the resources below.
In order to train and test our machine learning models, we need to split our data into two sets: training and testing. The training data is used to teach the model, while the testing data is used to evaluate how well the model performs. Splitting the data in this way allows us to make sure that our models are generalizable, and not just good at memorizing the training data.
There are many ways to split data, but the most common is to simply split it randomly, with each instance having an equal chance of being in either the training or testing set. Another common method is stratified sampling, whichSplit the dataset by creating a random subset of instances for each label (i.e., value of the target variable). This ensures that each label is represented in both the training and test sets. This is especially important if some labels are much rarer than others.
Once we’ve decided on a method, we need to actually split the data. This can be done using a library like scikit-learn’s train_test_split function:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
This will split our data into 80% training and 20% testing sets. We can also specify what proportion we want for our validation set if we’re doing cross-validation.
Model training is the process of learning the parameters of a model from data. The process of model training is used to find the optimal values of the parameters that minimize a loss function. The training process can be supervised, semi-supervised, or unsupervised.
Model evaluation is a process of quantifying the performance of machine learning models. The goal is to estimate the model’s generalizability to new data. The strategies used to evaluate a model varies depending on the type of machine learning problem being solved (e.g. regression, classification, or clustering).
In general, all model evaluation approaches have the same steps:
1. Select an appropriate performance metric for the problem being solved
2. Divide the dataset into train and test sets
3. Train the model on the training set
4. Evaluate the model’s performance on the test set using the chosen performance metric
model tuning is the process of finding the set of optimal hyperparameters for a machine learning model. The goal of tuning is to improve the performance of the model on unseen data.
There are a few different methods that can be used to tune machine learning models, including grid search, random search, and Bayesian optimization. Each method has its own advantages and disadvantages, so it’s important to choose the right method for your particular problem.
Grid search is a brute force method that systematically tries every combination of hyperparameters in order to find the best set for the model. This method can be very time-consuming, but it guarantees that the best set of hyperparameters will be found.
Random search is a more efficient method that randomly samples from a space of possiblehyperparameters. This method can find good sets of hyperparameters more quickly than grid search, but it doesn’t guarantee that the best set will be found.
Bayesian optimization is a sophisticated technique that uses previous evaluations of the objective function to intelligently choose new points to sample. This method can often find near-optimal sets of hyperparameters in fewer iterations than both grid search and random search.
Saving and loading models
You can save a model to disk so you can load it later and use it to make predictions. This is really useful if you want to deploy your model in a production environment, or if you want to share your model with someone else.
There are two ways to save models in scikit-learn: using the pickle module, or using joblib.
Pickle is the default way to save models in scikit-learn, and it has some advantages:
-Pickle is a standard module, so you don’t need to install anything extra to use it.
-Pickle files are often smaller than joblib files.
-Pickle files can be read by any Python program, so you don’t need scikit-learn to read them.
Joblib is more efficient than pickle (and sometimes faster), but it can only be used to save models that have been trained with scikit-learn. Additionally, joblib serializes python objects differently than pickle, so you can’t read joblib files using the pickle module.
After training a model, you will want to deploy it to a production environment where it can be used by target users. Depending on your application, this could involve providing a web interface for users to interact with the model, or deploying the model on a server that can provide prediction results in real time.
There are a few things to consider when deploying a machine learning model:
-How will users interact with the model?
-What performance characteristics are required?
-How often will the model need to be updated?
-What infrastructure is available for deployment?
These factors will guide your decisions about how to deploy your machine learning model.
There are a few advanced topics that we didn’t have time to cover in this course, but are worth mentioning. If you’re interested in pursuing machine learning, you should look into these concepts.
-Regularization: This is a technique used to prevent overfitting, which is when a model performs well on training data but not on new, unseen data.
-Ensemble methods: This is a technique where you train multiple models and combine their predictions. This can often lead to better performance than any single model.
-Deep learning: This is a type of machine learning that uses neural networks with many layers. Deep learning has been responsible for some of the most impressive results in machine learning in recent years.
Keyword: Basic Knowledge for Machine Learning