Updating Spyder takes forever

Spyder is a Python IDE that is bundled together with the Anaconda distribution.

There are some problems that are commonly faced when it comes to updating Spyder. One way to update Spyder is to open Anaconda Navigator and click the settings button which has an option to update Spyder. But the problem is that the process can take a very long time. The process shows that it is “loading packages of /User/…/opt/anaconda3”.

Updating Spyder is constricted by …

Another way to update Spyder is to type “conda update spyder” in the terminal. A problem that can crop up is the error message: “updating spyder is constricted by …

Anaconda stuck updating Spyder [Solved]

For my case, it turns out that the version of Anaconda Navigator is outdated. Hence, I first updated Anaconda Navigator to the latest version.

Then, instead of clicking “Update application” which still didn’t quite work, we click on “Install specific version” and choose the latest version of Spyder (Spyder 4.1.5 in this case).

Then, the updating of Spyder in Anaconda Navigator worked perfectly!

How to update Spyder using Anaconda-Navigator: Click “Install specific version” instead of “Update application”.

Best Udemy Data Science / Machine Learning / AI Courses

During this current lockdown period it is a good idea to pick up a data science skill. Most occupations can benefit from such a skill, including engineers, accountants, teachers, even students. Who knows, one day you may find deep learning useful!

In this page we introduce various Udemy courses (which come with certificates that you can put on your LinkedIn profile) that are the best in their class, be it for data science, machine learning (including deep learning), and AI (Artificial Intelligence).

Best Udemy Python Course

Currently, Python is the most popular language for data science and machine learning.  R is the second most popular language, and is especially good for statistics.

Hence, this Machine Learning A-Z™: Hands-On Python & R In Data Science Course is perfect as it introduces two of the most popular programming languages in one course! You will learn Machine Learning (ML) in the process as well, which is a great bonus.

If you only want to focus on Python, then check out 2020 Complete Python Bootcamp: From Zero to Hero in Python. It is designed to bring you from zero knowledge to a respectable expert in Python if you complete the course and exercises.

Best Udemy courses for data science

In the Python for Data Science and Machine Learning Bootcamp  course, students can learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more! The aforementioned packages are all classic and popular in data science, data analysis and data visualization.

The Data Science Course 2020: Complete Data Science Bootcamp is another bootcamp style course that gives you complete Data Science training in: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning. It is especially suitable for beginners, as well as intermediate students who need to brush up on their skills.

Best Udemy course for Deep Learning

Deep learning (DL) is a subbranch of machine learning that is recently very hot and popular due to its superior accuracy in tasks such as image classification and NLP (natural language processing).

The Deep Learning A-Z™: Hands-On Artificial Neural Networks allows students to learn how to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included, which is very important. Essentially, you can use and modify the templates to suit your individual task at hand.

Complete Guide to TensorFlow for Deep Learning with Python is a course for learn how to use Google’s Deep Learning Framework – TensorFlow with Python! Solve problems with cutting edge techniques! TensorFlow is one of the more popular deep learning framework, and is slightly ahead in popularity compared to its closest rival, PyTorch.

Udemy course benefits

The first benefit of Udemy courses, is that you get to learn content from the top trainers. Often, these courses are superior to free YouTube content, and may be even better than the courses in your school.

The second benefit is that Udemy provides a certificate upon completion that you can list in your CV, as well as put in your LinkedIn profile. This is especially important if you are trying to transition into a data scientist job from another field, like engineering or physical sciences.

What is your favorite Udemy course for AI/ML/DL? Feel free to comment below!

AI can’t predict how a child’s life will turn out even with a ton of data

Despite the great hype of AI, there are still many limitations of what AI can do. Real life is too complicated for a machine to figure out, at least currently.

Source: MIT Tech Review

Hundreds of researchers attempted to predict children’s and families’ outcomes, using 15 years of data. None were able to do so with meaningful accuracy.

Now a new study published in the Proceedings of the National Academy of Sciences casts doubt on how effective this approach really is. Three sociologists at Princeton University asked hundreds of researchers to predict six life outcomes for children, parents, and households using nearly 13,000 data points on over 4,000 families. None of the researchers got even close to a reasonable level of accuracy, regardless of whether they used simple statistics or cutting-edge machine learning.

“The study really highlights this idea that at the end of the day, machine-learning tools are not magic,” says Alice Xiang, the head of fairness and accountability research at the nonprofit Partnership on AI.

Best Pattern Recognition and Machine Learning Book (Bishop)

Pattern Recognition and Machine Learning (Information Science and Statistics)

The above book by Christopher M. Bishop is widely regarded as one of the most comprehensive books on Machine Learning. At over 700 pages, it has coverage of most machine learning and pattern recognition topics.

It is considered very rigorous for a machine learning (data science) book, but yet has a lighter touch than a pure mathematics or theoretical computer science book. Hence, it is perfect as a reference book or even textbook for students self learning the subject from the ground up (i.e. students who want to understand instead of just blindly apply algorithms).

A brief overview of the contents covered (taken from the contents page of the book):

  1. Introduction

  2. Probability Distributions

  3. Linear Models for Regression

  4. Linear Models for Classification

  5. Neural Networks

  6. Kernel Methods

  7. Sparse Kernel Machines

  8. Graphical Models

  9. Mixture Models and EM

  10. Approximate Inference

  11. Sampling Methods

  12. Continuous Latent Variables

  13. Sequential Data

  14. Combining Models