Data Science & Machine Learning

1.What is the meaning of term weight initialization in neural networks? In neural networking, weight initialization is one of the essential factors. A bad weight initialization prevents a network from learning. On the other side, a good weight initialization helps in giving a quicker convergence and a better overall error. Biases can be initialized to zero. The standard rule for setting the weights is to be close to zero without being too small. 2.What is Cross-validation in Machine Learning? Cross-validation allows a system to increase the performance of the given Machine Learning algorithm. This sampling process is done to break the dataset into smaller parts that have the same number of rows, out of which a random part is selected as a test set and the rest of the parts are kept as train sets. Cross-validation consists of the following techniques: • Holdout method • K-fold cross-validation • Stratified k-fold cross-validation • Leave p-out cross-validation 3.What is a Self-Join? A self-join is a type of join that can be used to connect two tables. As a result, it is a unary relationship. Each row of the table is attached to itself and all other rows of the same table in a self-join. As a result, a self-join is mostly used to combine and compare rows from the same database table. 4. What are the types of views in SQL? In SQL, the views are classified into four types. They are: Simple View: A view that is based on a single table and does not have a GROUP BY clause or other features. Complex View: A view that is built from several tables and includes a GROUP BY clause as well as functions. Inline View: A view that is built on a subquery in the FROM clause, which provides a temporary table and simplifies a complicated query. Materialized View: A view that saves both the definition and the details. It builds data replicas by physically preserving them.

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1. What is Dimensionality Reduction? In the real world, Machine Learning models are built on top of features and parameters. These features can be multidimensional and large in number. Sometimes, the features may be irrelevant and it becomes a difficult task to visualize them. This is where dimensionality reduction is used to cut down irrelevant and redundant features with the help of principal variables. These principal variables conserve the features, and are a subgroup, of the parent variables. 2.What is the bin in tableau? Bins in tableau are containers of equal size used to store data values fitting in bin size. In other words, bins group the data into groups of equal size or data which can be used in systematic viewing of data. All the discrete fields in tableau can also be considered as set of bins. 3.What’s a Fourier transform? A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. Or as this more intuitive tutorial puts it, given a smoothie, it’s how we find the recipe. The Fourier transform finds the set of cycle speeds, amplitudes, and phases to match any time signal. A Fourier transform converts a signal from time to frequency domain—it’s a very common way to extract features from audio signals or other time series such as sensor data. 4. What are Superkey and candidate key in SQL? A super key may be a single or a combination of keys that help to identify a record in a table. Know that Super keys can have one or more attributes, even though all the attributes are not necessary to identify the records. A candidate key is the subset of Superkey, which can have one or more than one attribute to identify records in a table. Unlike Superkey, all the attributes of the candidate key must be helpful to identify the records.

You don't need to buy a GPU for machine learning work! There are other alternatives. Here are some: 1. Google Colab 2. Kaggle 3. Deepnote 4. AWS SageMaker 5. GCP Notebooks 6. Azure Notebooks 7. Cocalc 8. Binder 9. Saturncloud 10. Datablore 11. IBM Notebooks Spend your time focusing on your problem.💪💪 Let others worry about the hardware!!

Python Machine Learning Projects 👇👇 https://t.me/Programming_experts/151

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Some of the essential libraries of Python that are used in Data Science Numpy SciPy Pandas Matplotlib Keras TensorFlow Scikit-learn

Important Topics to become a data scientist [Advanced Level] 👇👇 1. Mathematics Linear Algebra Analytic Geometry Matrix Vector Calculus Optimization Regression Dimensionality Reduction Density Estimation Classification 2. Probability Introduction to Probability 1D Random Variable The function of One Random Variable Joint Probability Distribution Discrete Distribution Normal Distribution 3. Statistics Introduction to Statistics Data Description Random Samples Sampling Distribution Parameter Estimation Hypotheses Testing Regression 4. Programming Python: Python Basics List Set Tuples Dictionary Function NumPy Pandas Matplotlib/Seaborn R Programming: R Basics Vector List Data Frame Matrix Array Function dplyr ggplot2 Tidyr Shiny DataBase: SQL MongoDB Data Structures Web scraping Linux Git 5. Machine Learning How Model Works Basic Data Exploration First ML Model Model Validation Underfitting & Overfitting Random Forest Handling Missing Values Handling Categorical Variables Pipelines Cross-Validation(R) XGBoost(Python|R) Data Leakage 6. Deep Learning Artificial Neural Network Convolutional Neural Network Recurrent Neural Network TensorFlow Keras PyTorch A Single Neuron Deep Neural Network Stochastic Gradient Descent Overfitting and Underfitting Dropout Batch Normalization Binary Classification 7. Feature Engineering Baseline Model Categorical Encodings Feature Generation Feature Selection 8. Natural Language Processing Text Classification Word Vectors 9. Data Visualization Tools BI (Business Intelligence): Tableau Power BI Qlik View Qlik Sense 10. Deployment Microsoft Azure Heroku Google Cloud Platform Flask Django ENJOY LEARNING 👍👍