Machine Learning

Introduction What profession did Harvard call the Sexiest Job of the 21st Century ? That’s right… the data scientist . Ah yes, the ever-mysterious data scientist. So what exactly is the data scientist’s secret sauce, and what does this “sexy” person do at work every day? This article is intended to help define the data scientist role, including typical skills, qualifications, education, experience, and responsibilities. This definition is somewhat loose since there isn’t a standardized definition of the data scientist role, and given that the ideal experience and skill set are relatively rare to find in one individual. This definition can be further confused by the fact that there are other roles sometimes thought of as the same, but are often quite different. Some of these include data analyst , data engineer , and so on. More on that later. A data scientist’s level of experience and knowledge in each often varies along a scale ranging from beginner, to proficient , and to...

First of all, get into an Environment of Anaconda weather Spyder, Jupyter Notebook and for Business Analytics, it can be Orange. To head-start with this, first: Import Libraries Importing Dataset Distribute dataset to test data and Train dataset Feature Scaling(If needed.) Import Machine Learning Model like SVM, Linear Regression, etc for the dataset. Predicting the Test set results Visualizing the Training set results Visualizing the Test set results These are the main eight Steps to test — train the dataset and get your model Train. For simplicity, Here is the code for Logistic Regression: # Simple Linear Regression # Importing the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing the dataset mydataset = pd.read_csv(‘salary.csv’) X = mydataset.iloc[:, :-1].values y = mydataset.iloc[:,:].values # Splitting the dataset into the Training set and Test set from sklearn.cross_validation import train_test_split X_train, X_...

60 Types of Data Visualization and their Usage To know the visualization you can see at   https://lnkd.in/fzHe8QC  . To know how to Implement Data Visualization in Business? https://lnkd.in/fYUCzgC For practical but less technical resource you can see links below  Know Data Science  Datanest on LinkedIn: "Want to know more on the real-world examples on data science implementation? You… March 26, 2019: Datanest posted on LinkedIn lnkd.in 2. Understand How to answer Why  Datanest on LinkedIn: "To gain value from analytics, we not only need to answer what, but also why… April 3, 2019: Datanest posted on LinkedIn lnkd.in 3. Know Machine Learning Key Terminology https://lnkd.in/fCihY9W   4. Understand Machine Learning Implementation  Datanest on LinkedIn: "Confuse how to convert machine learning to business solution. This is our 7… May 2, 2019: Datanest posted on LinkedIn lnkd.in 5. Machine Learning Applications on Marketing  ...

The Main Courses out of which each person has to do so, to get into Machine Learning are: 1. Machine Learning Certification by Stanford University (Coursera) 2.  Deep Learning Specialization by deeplearning.ai (Coursera) 3. Machine Learning DataScience Certification from Harvard University (edX) 4. Machine Learning DataScience Certification from IBM (Coursera) 5. Machine Learning with Tensorflow on Google Cloud Platform 6. Machine Learning Certification by University of Washington (Coursera) 7. Machine Learning Training A-Z: Hands-On Python and R for Data Science (Udemy) 8. Mathematics of Machine Learning  (Coursera) 9. Data Science Specialization - John Hopkins University  (Coursera) 10. Python for Data Science and Machine Learning (Udemy) 11. Deep Learning Training A-Z: Hands-On Python and R for Data Science (Udemy) 12. Introduction to Machine Learning (IIT - KGP) by Prof Sudeshna Sarkar (NPTEL) 13. Introduction to Machine Learning (IIT - M) by Prof. Balaraman Ravindr...

Have you heard people talking about machine learning but only have a fuzzy idea of what that means? Are you tired of nodding your way through conversations with co-workers? Let’s change that! This guide is for anyone who is curious about machine learning but has no idea where to start. I imagine there are a lot of people who tried reading the Wikipedia article, got frustrated and gave up wishing someone would just give them a high-level explanation. That’s what this is. The goal is to be accessible to anyone — which means that there’s a lot of generalizations. But who cares? If this gets anyone more interested in ML, then mission accomplished. What is machine learning? Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the...