Great book to deep dive on the machine learning algorithms.⠀
Don't miss this one if you want to move to senior data science roles, it's required reading.⠀
Fortunately, it's free too.⠀
Grab the PDF here -> http://dsdj.co/esl-book⠀
What machine learning books are your favorites?⠀
Let me know in the comments below! 👇
4 weeks ago
Happy Tuesday friends 🙌🏼 Today I am digging deep in my Health App design.
How are you spending your day?
Simple Multilayer Perceptron ✨⠀
Consists of at least three layers of nodes: an input layer, a hidden layer and an output layer. ⠀
- Except for the input nodes, each node is a neuron that uses a nonlinear activation function ⠀
- MLP utilizes a supervised learning technique called Backpropagation for training⠀
- Takes the inputs, multiplies them by their weights, and computes their sum⠀
- Adds a bias factor, the number 1 multiplied by a weight⠀
- Feeds the sum through the activation function⠀
- The result is the perceptron output⠀
Have you ever implemented a Simple Multilayer Perceptron?
Achieving great things takes time...⠀
When you are most frustrated, most fed up, and most want to quit... don't.⠀
That's when everyone else quits.⠀
But that's not when we quit.⠀
That's when we keep going.⠀
Because we know that there is greatness beyond our most difficult struggles.⠀
We just have to keep pushing ahead a little bit further and all of our hard work will be worth it.⠀
2 weeks ago
Top 10 Algorithms to Know to Become a Data Scientist⠀
Interested in becoming a data scientist?⠀
These are the 10 most important machine learning algorithms that you need to master to break into the field:⠀
• Linear regression - https://en.wikipedia.org/wiki/Linear_regression⠀
• Logistic regression - https://en.wikipedia.org/wiki/Logistic_regression⠀
• SVM - https://en.wikipedia.org/wiki/Support_vector_machine⠀
• Random forest - https://en.wikipedia.org/wiki/Random_forest⠀
• Gradient boosting - https://en.wikipedia.org/wiki/Gradient_boosting⠀
• PCA - https://en.wikipedia.org/wiki/Gradient_boosting⠀
• K-means clustering - https://en.wikipedia.org/wiki/K-means_clustering⠀
• Collaborative filtering - https://en.wikipedia.org/wiki/Collaborative_filtering⠀
• kNN - https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm⠀
• ARIMA - https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average⠀
Bonus: Neural networks - https://en.wikipedia.org/wiki/Artificial_neural_network⠀
The course notes and book that I first used to learn machine learning⠀
➡ http://l2r.cs.uiuc.edu/~danr/Teaching/CS446-17/lectures.html ⠀
👆 These notes do an amazing job teaching the algorithms with deeper mathematical rigor while still being easy to follow.⠀
Master the above algorithms and you'll be well on your way to becoming a data scientist.
3 weeks ago
What programming language should I learn first? ⠀
This is a question I get often in my DMs so I thought I’d open the discussion here. When I first started to learn code I was 16 and I was taking a Computer Science major. There we learned to code in Pascal. For that I had to rewire my brain to be able to work with Objects and Classes and Variables. Now I work in Python and I am currently learning Java. ⠀
When it comes to learning to code it isn’t about how many languages you know, it is about how well you can pick up on patterns and solve problems. ⠀
I want to hear from you, which programming language you first learned and what you would tell your younger self starting out to code👇🏼
Kicking off the lecture on the “Future of Work” by Julia Gometz of the “brandful | code” initiative. Thank you so much to all students, staff, faculty, and community partners for attending.
1 hour ago
We are hiring a Director of Marketing Data Analytics in Uniondale, NY! The role involves monitoring and delivering clear, reliable digital marketing performance metrics, understanding complex business data, and leveraging the data to understand and improve marketing business processes and initiatives.
To apply, visit arbor.com/careers
Inspiring talks last week at #WiDS2019 St. Louis. Thank you WiDS ambassadors Margarit Khachatryan, Tamarah Usher, and DeAnna Tripton for making it happen! #widsstl
#repost @archcity.misfit The Women in Data Science #STL was amazing and I won this sweet bag from @teksystems!
I'm excited to bring my learnings back to @hlkagency 📊
#widsstl2019 #widsstl #datascience
Le système Isotype
Isotype 'Picture dictionary'.
Otto et Marie Neurath
La collection Isotype documente les méthodes de dessin des données chiffrées et à joué un rôle majeur dans le design du 20e siècle.
4000 pictos dessinés par Gerd Arntz, 1929 à 1933.
3 hours ago
💬 "Beyond the data, what can we understand?" 📽️ In this new "OECD Expert Talks" episode, our Head of Communications Monitoring & Impact Evaluation @cami.raymond discusses her acting career + why social listening is essential for an international organisation like ours.
3 hours ago
Carlos, quién se incorporó a Roland Berger en septiembre, ha estado una semana de curso 👨🏻🏫 de Data Analytics💻 en Munich 🇩🇪. En el curso aprendió a utilizar las herramientas clave de Data Analytics 📊 y conoció a otros consultores de la red internacional de oficinas de Roland Berger. 🌍 🍻