Data Science & Machine Learning

Supervised learning requires a training set to teach models to yield the desired output. Training dataset includes inputs and correct outputs, which allow the desired model to learn over time. The algorithm also measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized.

What is gradient boosting trees? Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.

What happens when we have correlated features in our data? In random forest, since random forest samples some features to build each tree, the information contained in correlated features is twice as much likely to be picked than any other information contained in other features. In general, when you are adding correlated features, it means that they linearly contains the same information and thus it will reduce the robustness of your model. Each time you train your model, your model might pick one feature or the other to "do the same job" i.e. explain some variance, reduce entropy, etc.

Udacity's AWS Machine Learning Scholarship to learn machine learning fundamentals for free 👇👇 bit.ly/3vXVYLG Udacity has partnered with AWS to launch the new AWS Machine Learning Scholarship program to enable developers of all skill levels learn the fundamentals of machine learning - for free! Applicants will gain access to the AWS Machine Learning Foundations course and top performers will be selected to receive a full scholarship to the AWS Machine Learning Engineer Nanodegree program. Additionally, the first 150 students to successfully complete the course will receive an AWS DeepLens device and the first 2,500 who enroll in the course will receive $35 in AWS credits! Applications are open now and close on July 12.

Spatial Analysis & Geospatial Data Science in Python 👇👇 https://t.me/free4unow_backup/158

What is feature selection? Why do we need it? Feature Selection is a method used to select the relevant features for the model to train on. We need feature selection to remove the irrelevant features which leads the model to under-perform.

How Tesla Self Driving Car Works? 👇👇 bit.ly/3wTVLdr Register for Free session about trending technologies such as YOLO, Neural Networks, understanding the limitless applications of computer vision, and implementing all the above with ease.

What are the main parameters of the decision tree model? • maximum tree depth • minimum samples per leaf node • impurity criterion

What are the decision trees? This is a type of supervised learning algorithm that is mostly used for classification problems. Surprisingly, it works for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets. This is done based on most significant attributes/ independent variables to make as distinct groups as possible. A decision tree is a flowchart-like tree structure, where each internal node (non-leaf node) denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (or terminal node) holds a value for the target variable. Various techniques : like Gini, Information Gain, Chi-square, entropy.

Three different learning styles in machine learning algorithms: 1. Supervised Learning Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time. A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data. Example problems are classification and regression. Example algorithms include: Logistic Regression and the Back Propagation Neural Network. 2. Unsupervised Learning Input data is not labeled and does not have a known result. A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity. Example problems are clustering, dimensionality reduction and association rule learning. Example algorithms include: the Apriori algorithm and K-Means. 3. Semi-Supervised Learning Input data is a mixture of labeled and unlabelled examples. There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions. Example problems are classification and regression. Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.

Register now to book your slot absolutely free 👇👇 https://bit.ly/2SLpFlw

Data Structures Free Bootcamp 👇👇 https://forms.gle/mbTfKXBgSKubSZqv6 Registration link

Best Channel To Download All Programming Books And Courses with codes t.me/DataScience_Books

Can you explain how cross-validation works? Cross-validation is the process to separate your total training set into two subsets: training and validation set, and evaluate your model to choose the hyperparameters. But you do this process iteratively, selecting differents training and validation set, in order to reduce the bias that you would have by selecting only one validation set