Data Science & Machine Learning

Some useful Machine Learning projects for practice https://elitedatascience.com/machine-learning-projects-for-beginners https://hackernoon.com/top-5-machine-learning-projects-for-beginners-47b184e7837f https://www.springboard.com/blog/machine-learning-projects https://www.ubuntupit.com/top-20-best-machine-learning-projects-for-beginner-to-professional

Learn how Amazon Optimise Delivery Routes using Genetic Algorithm 👇👇 https://bit.ly/3izxvZJ Free Live Session with Certificate

Some interview questions related to Data science 1- what is difference between structured data and unstructured data. 2- what is multicollinearity.and how to remove them 3- which algorithms you use to find the most correlated features in the datasets. 4- define entropy 5- what is the workflow of principal component analysis 6- what are the applications of principal component analysis not with respect to dimensionality reduction 7- what is the Convolutional neural network. Explain me its working

🤓 Technical Python concepts tested in the data science job interviews are: - Data types. - Built-in data structures. - User-defined data structures. - Built-in functions. - Loops and conditionals. - External libraries (Pandas). Source Article: https://www.kdnuggets.com/2021/07/top-python-data-science-interview-questions.html

What are the benefits of a single decision tree compared to more complex models? easy to implement fast training fast inference good explainability

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.

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.

The session is going to go LIVE in 5 mins So join in now ! Link - https://youtu.be/a8lGmSLcfWU

Do you want to Learn how to build a winning resume ? Learn the basics from an expert in our mentor conference 2021 Resume session today! Link to Join - https://youtu.be/a8lGmSLcfWU If you are interested in getting a certificate and resources then kindly register at www.foreignadmits.com/mentorconference2021 Note - Certificates will only be given to registered LIVE attendees See you at the mentor conference Join our community to get updates about other sessions - https://t.me/foreignadmits_community

Quiz Explaination Supervised Learning: All data is labeled and the algorithms learn to predict the output from the input data Unsupervised Learning: All data is unlabeled and the algorithms learn to inherent structure from the input data. Semi-supervised Learning: Some data is labeled but most of it is unlabeled and a mixture of supervised and unsupervised techniques can be used to solve problem. Unsupervised learning problems can be further grouped into clustering and association problems. Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior. Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy A also tend to buy B.

What are the main parameters of the random forest model? max_depth: Longest Path between root node and the leaf min_sample_split: The minimum number of observations needed to split a given node max_leaf_nodes: Conditions the splitting of the tree and hence, limits the growth of the trees min_samples_leaf: minimum number of samples in the leaf node n_estimators: Number of trees max_sample: Fraction of original dataset given to any individual tree in the given model max_features: Limits the maximum number of features provided to trees in random forest model

🎓 Introduction to Deep Learning (by MIT) 🎓 This is one of the top high-quality courses to learn the foundational knowledge of deep learning. All lectures have been uploaded. 100% Free! https://youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI

What are the main parameters in the gradient boosting model? There are many parameters, but below are a few key defaults. learning_rate=0.1 (shrinkage). n_estimators=100 (number of trees). max_depth=3. min_samples_split=2. min_samples_leaf=1. subsample=1.0.