Data Science Tips for Beginners!

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data ➼ The Most Complete List of Best AI Cheat Sheets. ➼ Neural Networks Graphs Cheat Sheet ➼ Machine Learning Overview MACHINE LEARNING IN EMOJI ➼ SUPERVISED

I have summarized in this text best tips for data scientists to progress along their career. Whether you’re just starting or you want to go from junior to mid or mid to senior, there’s something new here for you. It's good to know about regression, Clustering,classification ensemble models however theses algorithms are now being taken care by Auto ML packages. Now, the hiring organisations are

Top 50 Machine Learning Interview Questions and Answers. 1) What is Machine learning? Ans - Machine learning is a branch of computer science which deals with system programming in order to automatically learn and improve with experience. For example: Robots are programmed so that they can perform the task based on data they gather from sensors. It automatically learns programs from data. 2) Mention the difference between Data Mining and Machine learning? Ans - Machine learning relates with

📇Top Machine Learning Algorithms📉 Naïve Bayes Classifier Algorithm K Means Clustering Algorithm Support Vector Machine Algorithm Apriori Algorithm Linear Regression Logistic Regression Artificial Neural Network Random Forests Decision

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

Solved a DS/Algo question & forget it after a week? Don't worry I have a SECRET tip for you. "update your `mind cache` with Fibonacci Sequence Interval, starting from index=2" Explanation: 0. Suppose you solved a problem on

NumPy NumPy is the fundamental package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object sophisticated (broadcasting) functions tools for integrating C/C++ and Fortran code useful linear algebra, Fourier transform, and

An outlier is an observation that is unlike the other observations. It is  rare, or distinct, or does not fit  in some way. Outliers can have many causes, such as:

Plotly's Python graphing library makes interactive, publication-quality graphs. Plotly is a company that makes visualization tools including a Python API library. Plotly's Python graphing library makes interactive, publication-quality graphs. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots,

These are the Mistakes that I generally did and learned from that. Undermining the Power of Statistics and Probability. Ignoring the Outliers and Missing Values or Using a general method to treat them. they have a strong pattern hidden inside them, so do not take them lightly. It is more Important to make our Data better instead of using fancy models. because if data is poor, the results will be

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! 👈

Python is my most favorite tool for Data Science. Learning Python will definitely help you in your Data Science journey. Here is a great 63 pages comprehensive Python document that should help you. All the best! 👍🏻 👉 Click Here to Download the Book! 👈

The good news is that there are countless tutorials and helpful reference guides all over the internet, ripe for perusal. But with the sheer amount of content to sift through, you’re bound to find a few bad apples. Instead of going through every AI video tutorial out there, let us do some of the work for you. Abhishek Thakur( https://www.youtube.com/user/abhisheksvnit/about ) Krish Naik ( https://www.youtube.com/user/krishnaik06 ) Siraj Raval ( https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A/about ) Simpli Learn ( https://www.youtube.com/user/Simplilearn ) Sentdex ( https://www.youtube.com/user/sentdex ) Lauis serrano ( https://www.youtube.com/channel/UCgBncpylJ1kiVaPyP-PZauQ )

'Data Science' is a very attractive career path today. A huge lot of individuals start walking on this path and majority of them fall in the trap of these luring mistakes. I also belong to this majority. That is why I am writing this post so that you learn from my mistakes as it is rightly said “Intelligent is the one who learns from others’ mistakes.” Learning theory first and implementing later : Like many others we start our journey by taking a course and learn ‘Data Science’. Mine was no different. Majority of these courses will give you lots and lots of theory. Many courses have quizzes and assignments, but still the practical aspects lack in them because you don’t practically implement things along with the course. Theories are definitely important but they are of no use if you don’t implement and understand its actual use. Relying just on the

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