Data Science Tips for Beginners!

  Q1. You are given a train data set having 1000 columns and 1 million rows. The data set is based on a classification problem. Your manager has asked you to reduce the dimension of this data so that model computation time can be reduced. Your machine has memory constraints. What would you do? (You are free to make practical assumptions.) Answer: Processing a high dimensional data on a limited memory machine is a strenuous task, your interviewer would be fully aware of that. Following are the methods you can use to tackle such situation: Since we have lower RAM, we should close all other applications in our machine, including the web browser, so that most of the memory can be put to use. We can randomly sample the data set. This means, we can create a smaller data set, let’s say, having 1000 variables and 300000 rows and do the computations. To reduce dimensionality, we can separate the numerical and categorical variables and remove the correlated variables. For numerical variable...

  Data preprocessing  is a step in the data mining and data analysis process that takes raw data and transforms it into a format that can be understood and analyzed by computers and machine learning. When we talk about  data , we usually think of some  large datasets  with some rows and columns. While that is a likely scenario, it is not always the case — data could be in so many different forms:  Structured Tables, Images, Audio files, Videos , etc. Machines don’t understand text, image, or video data as it is, they understand 1s and 0s. Real-world data also contains noises, missing values, etc. which cannot be directly used for ML models. Hence,  data preprocessing  is required for cleaning the data and making it suitable for an ML model which increases the accuracy and efficiency of the model. It involves the following steps: Getting the Dataset Importing Libraries Importing Dataset Data Quality Assessment: i) Finding and Processing Missing/Inc...

  List of Common Machine Learning Algorithms Here is the list of commonly used machine learning algorithms. These algorithms can be applied to almost any data problem: Linear Regression Logistic Regression Decision Tree SVM Naive Bayes kNN K-Means Random Forest Dimensionality Reduction Algorithms Gradient Boosting algorithms GBM XGBoost LightGBM CatBoost 1. Linear Regression It is used to estimate real values (cost of houses, number of calls, total sales etc.) based on continuous variable(s). Here, we establish relationship between independent and dependent variables by fitting a best line. This best fit line is known as regression line and represented by a linear equation Y= a *X + b. https://www.analyticsvidhya.com/wp-content/uploads/2015/08/Linear_Regression.png 2. Logistic Regression Don’t get confused by its name! It is a classification not a regression algorithm. It is used to estimate discrete values ( Binary values like 0/1, yes/no, true/false ) based on given set of indepe...

  1 . Sentiment Analyzer of Social Media This is one of the interesting and innovative machine learning projects. As, social media like Facebook, Twitter, and YouTube is the ocean of big data. Therefore, mining these data can be beneficial in a number of ways to understand user sentiments and opinions. This project can be effective for digital marketing and branding to understand the opinion or reaction for a product or service of a customer. 2 . Music Recommendation System Are you a lover of music? Always love to listen to your favorite one? Then, you will be glad to know about this interesting machine learning project idea. This can also be an innovative project. The goal of this project is to recommend music based on user listening history. 3 . Credit Card Fraud Detection Project Companies that involve a lot of transactions with the use of cards need to find anomalies in the system. The project aims to build a fraud detection model on credit cards. We will use the transaction an...

  1. Pick the brain of an expert. There are myriads of ways to learn data science. You can read articles, watch videos, enroll in onli n e courses, turn up at meetups, etc. But one thing that you cannot “learn” is the  experience . That you have to gain throughout years of working in the field. There is much to learn from Data science experts, their experience in managing end-to-end machine learning and deep learning projects, their philosophy when constructing a data science team from scratch,

  Statistics & Probability Intro-to-Descriptive-Statistics How To Ace Data Science Interviews: Statistics

  Non-Technical Skills These skills won’t require as much technical training or formal certification, but they’re foundational to the rigorous application of data science to business problems. Even the most technically skilled data scientist needs to have the following soft skills to thrive today.

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One of the ways to build a robust and intelligent chatbot system is to feed question answering datasets during training the model. Question answering systems provide real-time answers that are essential and can be said

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Break into Data Science Become A Data Science Expert Build up your ability to ingest, transform, visualize and model datasets. Whether you’re new to data science, or just need to brush up and expand your knowledge. Learn mathematics, statistics, SQL, Python, data cleaning and visualization, and machine and

It's a github repository which contains huge collection of books on: Computer Vision Deep Learning Machine Learning Math NLP Python Reinforcement Learning. 👉 Click Here to View the Collection! 👈

Deep Learning - Coursera Course Notes Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network. Deep learning is used across all industries for a number of different tasks. Commercial apps that use image recognition, open-source platforms with consumer recommendation apps and medical research tools that explore the possibility of reusing drugs for new ailments are a few of the examples of deep learning incorporation. 👉 Click Here to Download the Notes! 👈

I will highlight some of the key changes with the latest version of Tensorflow 2.0 compared to earlier versions: Now Keras users who use multi-backend Keras with the TensorFlow backend are needed switch to tf.keras in TensorFlow 2.0. As it’s going to be better maintained. By default Tensorflow 2.0 will be having Eager execution that evaluates operations immediately, without building graphs. You can obviously switch to the style of building graphs, as it was in Tensorflow earlier as well It is now having a full lower-level API, which allows it to use for having custom training loops(which is needed by researchers a lot) With tf.GradientTape you can now do automatic differentiation One of the other features of Tensorflow is experimental support for mixed precision on GPUs and Cloud TPUs A clear consistent, intuitive, syntax across various APIs Tensorflow 2.1 bought support for TPU training in a simple and intuitive way which makes it’s a favourite for Kagglers like me Internal...

Great introduction to data science for total beginners "I wanna be a data scientist, but… how?". Where to start, and what to do step-by-step. Plus, the author gives a few recommendations for what classes to take and lists what skills are required to become a data scientist. Great visualizations too! I wish I had it a year ago. Instead, I had to go through hundreds of job postings to see what skills are required by the job market, and doing many-hours research on upvoted online courses. I found Kaggle and GitHub through my LinkedIn connections and learned them by myself (by the way, there are courses for everything). Good luck in your

Hello to everyone, We all know that the internet offers us very useful information. Sometimes we want to improve ourselves so much that the information is not enough for us. Therefore, I am writing links to help you reach free courses available on different platforms. I wish everyone the best in the way of being a data scientist. Congnitive Class Python for Data Science :  https://cognitiveclass.ai/courses/python-for-data-science/ Machine Learning with Python :   https://cognitiveclass.ai/courses/machine-learning-with-python/ Data Visualization with Python :   https://cognitiveclass.ai/courses/data-visualization-with-python/ Introduction to Data Science :   https://cognitiveclass.ai/courses/data-science-101/ Deep Learning Fundementals :   https://cognitiveclass.ai/courses/introduction-deep-learning/ Data Analysis with Python :   https://cognitiveclass.ai/courses/data-analysis-python/ Deep Learning with TensorFlow : ...