Source: Deep Learning on Medium
My Review Of The Hundred-Page Machine Learning Book by Andriy Burkov(first part)
Hello, My name is Zahra ELHAMRAOUI and this is My Review of THE HUNDRED-PAGE MACHINE LEARNING BOOK.
This Book was My first Book in learning ML, It is one of the best basic books every beginner should take what I am writing is just for me in other to make the pieces of information I’ve learned in place and to motivate beginners to start the book let’s jump into it.
what is Machine Learning?
Andriy Burkov defines Machine learning as a subfield of computer science that is concerned with building algorithms that, to be useful, rely on a collection of examples of some phenomenon. These examples can come from nature, be handcrafted by humans or generated by another algorithm.
Machine learning can also be defined as the process of solving a practical problem by gathering a dataset, and algorithmically building a statistical model based on that dataset.
Types of Learning :
1- Supervised Learning: the dataset is the collection of labeled examples , its goal is to use the dataset to produce a model that takes a feature vector x as input and outputs information that allows deducing the label for this feature vector. For instance, the model created using the dataset of people could take as input a feature vector describing a person and output a probability that the person has cancer.
2- Unsupervised Learning: he dataset is a collection of unlabeled examples
Again, x is a feature vector, and the goal of an unsupervised learning algorithm is to create a model that takes a feature vector x as input and either transforms it into another vector or into a value that can be used to solve a practical problem. For example, in clustering,
3- Semi-Supervised Learning: the dataset contains both labeled and unlabeled examples. Usually, the quantity of unlabeled examples is much higher than the number of labeled examples. The goal of a semi-supervised learning algorithm is the same as the goal of the supervised learning algorithm.
4- Reinforcement Learning: is a subfield of machine learning where the machine “lives” in an environment and is capable of perceiving the state of that environment as a vector of features. The machine can execute actions in every state. Different actions bring different rewards and could also move the machine to another state of the environment. The goal of a reinforcement learning algorithm is to learn a policy. A policy is a function f(similar to the model in supervised learning) that takes the feature vector of a state as input and outputs an optimal action to execute in that state. The action is optimal if it maximizes the expected average reward.
Fundamental Algorithms :
1-Linear Regression: It is a popular regression learning algorithm that learns a model which is a linear combination of features of the input example.
2-Logistic Regression: The first thing to say is that logistic regression is not a regression, but a classification learning algorithm. The name comes from statistics and is due to the fact that the mathematical formulation of logistic regression is similar to that of linear regression.
3-Decision Tree Learning: A decision tree is an acyclic graph that can be used to make decisions. In each branching node of the graph, a specific feature j of the feature vector is examined. If the value of the feature is below a specific threshold, then the left branch is followed; otherwise, the right branch is followed. As the leaf node is reached, the decision is made about the class to which the example belongs.
4-Support Vector Machine: This algorithm requires that the positive label (in
our case it’s “spam”) has the numeric value of +1 (one), and the negative label (“not_spam”) has the value of ≠1 (minus one).
5-k-Nearest Neighbors (kNN): is a non-parametric learning algorithm. Contrary to other learning algorithms that allow discarding the training data after the model is built, kNN keeps all training examples in memory.
I will complete it in part Two.
This book is really great and I am grateful to start with.
See you next week for part 2.
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