Educational Setting


Practical Deep Learning with PyTorch - Udemy

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Although many courses are very mathematical or too practical in nature, this course strikes a careful balance between the two to provide a solid foundation in deep learning for you to explore further if you are interested in research in the field of deep learning and/or applied deep learning. It is purposefully made for anyone without a strong background in mathematics. And for those with a strong background, it would accelerate your learning in understanding the different models in deep learning. This is not a course that emphasizes heavily on the mathematics behind deep learning.


Deep Learning Prerequisites: Linear Regression in Python

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This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you'll be returning to it for years to come. We will apply multi-dimensional linear regression to predicting a patient's systolic blood pressure given their age and weight. If you want more than just a superficial look at machine learning models, this course is for you.


Top 3 free online courses for Artificial Intelligence and Machine Learning

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Artificial Intelligence deals with the understanding of machines and programming them to do tasks autonomously as well as helping them get smarter. The course will teach you the fundamentals of Artificial Intelligence with insights on search, simulated annealing, logical planning and more. The course is for those who have tried machine learning and data science but are having trouble putting the ideas down in code. You will learn about Numpy (which is fundamental computing package for Python) where you will explore complex mathematical functions that can be performed in Python.


Regression Models Coursera

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About this course: 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.


Machine Learning for Data Analysis Coursera

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Lasso regression analysis is a shrinkage and variable selection method for linear regression models. The goal of lasso regression is to obtain the subset of predictors that minimizes prediction error for a quantitative response variable. To test a lasso regression model, you will need to identify a quantitative response variable from your data set if you haven't already done so, and choose a few additional quantitative and categorical predictor (i.e. Having a larger pool of predictors to test will maximize your experience with lasso regression analysis.


Student Experience Lead - Digital Marketing

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Focused on self-empowerment through learning, Udacity is making innovative technologies such as self-driving cars available to a global community of aspiring technologists, while also enabling learners at all levels to skill up with essentials like programming, web and app development. A Student Experience Lead is a educational strategist and program manager in one, with a strong pedagogical sense. You should take pride in ensuring that the students in your Nanodegree program receive the best possible learning experience. This specific Lead will take care of our Digital Marketing Nanodegree program and deal with the most exciting community of students in Brazil.


Udacity Robotics video series: Interview with Chris Anderson from 3D Robotics

Robohub

Mike Salem from Udacity's Robotics Nanodegree is hosting a series of interviews with professional roboticists as part of their free online material. This week we're featuring Mike's interview with Chris Anderson, Co-Founder and CEO of 3D Robotics. Chris is a former Wired magazine editor turned robotics company co-founder and CEO. You can find all the interviews here.


Qualitative Research Methods Coursera

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About this course: In this course you will be introduced to the basic ideas behind the qualitative research in social science. You will learn about data collection, description, analysis and interpretation in qualitative research. We will focus on the ingredients required for this process: data collection and analysis. You won't learn how to use qualitative methods by just watching video's, so we put much stress on collecting data through observation and interviewing and on analysing and interpreting the collected data in other assignments.


Going Back To School

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"Very few institutes in India like ISB and IIM-B are offering courses in innovation, while courses in specialisation in data management and data analytics etc are being offered by many premier institutes such as IIM-Ranchi and IIM-Bangalore," points out Mohan Tiwari, CEO and co-founder of Students' Destination. Taking teaching out of the classroom, Enactus India encourages experiential learning through students' interaction with communities. "This is driving a large number of working professionals to take up management programmes in various specialised domains. In addition, the IIM-Calcutta offers innovative management development programmes to meet the needs of the industry.


Improving your statistical inferences Coursera

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First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. In practical, hands on assignments, you will learn how to simulate t-tests to learn which p-values you can expect, calculate likelihood ratio's and get an introduction the binomial Bayesian statistics, and learn about the positive predictive value which expresses the probability published research findings are true. You will calculate effect sizes, see how confidence intervals work through simulations, and practice doing a-priori power analyses.