President Emmanuel Macron together with many Silicon Valley CEOs will kick off the VivaTech conference in Paris this week with the aim of showcasing the "good" side of technology. Our research highlights some of those benefits, especially the productivity growth and performance gains that automation and artificial intelligence can bring to the economy -- and to society more broadly, if these technologies are used to tackle major issues such as fighting disease and tackling climate change. But we also note some critical challenges that need to be overcome. To see just how big those shifts could be, our latest research analyzed skill requirements for individual work activities in more than 800 occupations to examine the number of hours that the workforce spends on 25 core skills today. We then estimated the extent to which these skill requirements could change by 2030, as automation and artificial technologies are deployed in the workplace, and backed up our findings with a detailed survey of more than 3,000 business leaders in seven countries, who largely confirmed our quantitative findings.

An average data scientist deal with lots of data daily, around 60–70% time spend on data cleaning, data munging and convert the data into suitable form so that we can apply machine learning model on that data. This blog focuses on applying machine learning models, including the preprocessing steps. Many Data science enthusiast ask me how to solve machine learning problem? Before applying the machine learning models, the data must be converted to a tabular form. There is two types of data Numerical variable and Categorical variable.

For almost all machine learning projects, the main steps of the ideal solution remains same. For each step, I was doing some research on the web depending on my business object and jotting down the best resources I ran across. The resources include Online Courses, Kernels from Kaggle, Cheat Sheets and Blog Posts. Below I've listed them and categorised by each step (all of the resources are free except the ones that have'paid' in the end):

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist's toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated.

The online fashion retail space continues to boom, as modern consumers increasingly lean towards a more convenient remote buying experience. However, to stay ahead of the competition, leading retail brands need to offer an ever evolving catalogue of seasons, styles and sizes on their platforms. This poses a challenge for inventory management, when constantly updating and removing available items daily. For warehouse staff, and catalogue managers, one of the biggest challenges is developing an efficient system to log new items while on the move, and quickly determine whether the item is already in stock. When dealing with such large catalogues, the traditional method of manually checking each garment from millions of items, would slow processes to a snail's pace.

Functional programming is a style of programming that is characterized by short functions, lack of statements, and little reliance on variables. You will learn what functional programming is, and how you can apply functional programming in Python. In this video course, we will learn what functional programming is, and how it differs from other programming styles, such as procedural and object-oriented programming. We will also learn why and when functional programming is useful, and why and when it makes programs unnecessarily complex. Then we go on to explore lambda expressions, which are short one-line functions, and are the purest form of functional programming that Python offers.

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

Have you decided to learn Python as your first programming language? Or just heard that Python is one of the best modern languages to learn? Python is an incredibly powerful language and that can be used in almost any situation. That's why I chose Python as the first course in the "If You Can Cook, You Can Code" Series. Are you a beginner to programming?

Andrew Ng is a hero and a role model for everyone who is starting the machine learning journey. One of his earliest Machine Learning courses saw lakhs of students enrolling and getting a huge boost to their careers. He is now back with a course in Deep Learning specialisation supported by his company Deeplearning.ai. Andrew Ng, one of the foremost artificial intelligence experts, is working hard to train more AI experts on a larger scale who can work across a range of industries. Ng has been an early adopter of online learning with the creation of Coursera.

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. The course is shy but confident: It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.