Linear Algebra for Data Analysis: Scalars, Vectors, Matrices and Tensors
Mathematical analysis: derivatives and gradients
Probabilistic theory and statistics
Multivariate calculus
Complex algorithms and optimizations
How does Machine Learning work?
The three foundations of a Machine Learning system are the model, the parameters and the learner.
The model is the system that makes the predictions
Parameters are the factors that are considered by the model to make predictions.
The student makes adjustments to the parameters and models to align the predictions with the results.
Let's build using two objects as an example, wine and beer, to understand what qatar phone number data Machine Learning is and how it works. Let's assume that the machine learning model here has to predict whether a drink is beer or wine. The selected parameters are the color of the drink and the alcohol percentage. The first step is:
Learn from the training set
This involves taking a sample dataset of various beverages for each specified color and alcohol percentage. Now, we need to define a description of each classification, be it beer or wine, in terms of the parameter value for each type. The model can use the description to decide whether a new beverage is beer or wine.
You can represent the values of the parameters, 'color' and 'alcohol percentage' as 'x' and 'y' respectively. So (x,y) defines the parameters of each drink in the training data. This set of data is called the training set. These values, when displayed on a graph, present a hypothesis in the form of a line, rectangle or polynomial that best fits the desired results.
The second step is to measure the error
Once the model is trained on a defined dataset, it needs to be checked for outliers or errors. We use a fresh dataset to perform this task. The result of this test should be one of these four:
True Positive: When the model predicts the condition when it is present
True Negative: When the model does not predict a condition when it is absent
False Positive: When the model predicts the condition when it is absent
False negative: When the model does not predict a condition when it is present
The sum of FP and FN and the total error of the model
Noise management
For the sake of simplicity, we have considered only two parameters to approach this machine learning problem, namely color and alcohol percentage. But in reality, you will need to consider hundreds of parameters and a large learning dataset to solve a machine learning problem.
The hypothesis created will have many more errors because of noise. Noise is an unwanted anomaly that disrupts the data set and weakens the learning process. The reasons why this happens are:
Large training dataset
Errors in input data
Errors in data labeling
Unobserved attributes that may affect classification but are not considered within the dataset due to lack of data
You can accept a certain level of training error due to noise to keep the hypothesis as simple as possible.
Testing and generalization
While it is possible that an algorithm or hypothesis fits well on a training set, it may fail when applied to another data set outside the training set. Therefore, it is important to find out whether the algorithm works on new data. Testing it on a new set of data is one way to judge this. Also, generalization refers to how well the model predicts results for a new set of data.
When we fit a hypothesis algorithm to its maximum simplicity, it may have fewer errors for the training data, but it may have more significant errors as it processes new errors. We call this underfitting. On the other hand, if the hypothesis is too complicated to accommodate the best fit for the training results, it may not generalize as well. This is a case of overfitting. In both cases, the results are fed back to train the model further.
Now we know about Machine Learning, terminology, how it works, how to get started, etc. Keep reading to discover the most used languages in the field.
What is the best programming language for Machine Learning?
Python is undoubtedly the best language for Machine Learning applications due to the numerous benefits mentioned below. Other languages that can also be used for Machine Learning are R, C++, Java, C#, Julia, Shell, TypeScript and Scala .
Python is famous for being easy to read and having low complexity compared to other . Machine learning applications involve complex concepts like calculus and linear algebra that take a lot of effort and time to implement. Python serves to reduce this burden by giving the machine learning engineer the ability to quickly implement ideas for validation. Another benefit of using Python in machine learning is the pre-built libraries. There are different packages for different types of applications.
Numpy , OpenCV and Scikit are used to work with images
NLTK together with Numpy and Scikit again when working with texts
Libraries for audio applications
Matplotlib, Seaborn and Scikit for data presentation
TensorFlow and Pytorch for Deep Learning applications
Scipy for scientific computing
Django for web application integration
Pandas for high-level data structures and analysis
Python is a versatile programming language that works on any platform, including Windows , MacOS, Linux, Unix, and others. If you migrate from one platform to another, your code will need to be adapted and changed a little, but once you do, it will be ready to work on the new platform.
