INTRODUCTION TO COMPUTATIONAL THINKING AND DATA SCIENCE

The course aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals,

https://ocw.mit.edu/courses/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/

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Convolutional Neural Network Tutorial (CNN) – Developing An Image Classifier In Python Using TensorFlow

Convolutional Neural Networks have wide applications in image and video recognition, recommendation systems and natural language processing.
This article will guide you through understanding it.

https://www.edureka.co/blog/convolutional-neural-network/#z9

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Introduction to Neural Networks and Deep Learning Course

Expand your knowledge and skills in Neural Networks and Deep Learning with this online free course. Build and train deep neural networks for industry-related problems using key calculations that underlie deep learning

#machine_learning #datascience #datanalysis #neural_networks #deep_learning #ai #pythoasks.

Course Link

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Udacity Artificial Intelligence Course

Here's an interesting course where you’ll learn the basics and applications of AI, including: machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.

Course Link: Enroll Now

#machine_learning #datascience #datanalysis #neural_networks #deep_learning #ai #python

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Introduction to Tensorflow and Keras

Enroll in this TensorFlow and Keras course to gain in-depth knowledge of TensorFlow, Keras, Neural Networks, and CNN. Learn to solve Deep Learning problems through sample demonstrations.

Course Link

#machine_learning #datascience #datanalysis #neural_networks #deep_learning #ai #python

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Data 8: Foundations of Data Science
UC Berkeley, Fall 2022

The UC Berkeley Foundations of Data Science course combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design.

The course is offered in partnership with the UC Berkeley Division of Computing, Data Science, and Society.

⏳ Duration: 15 weeks
✅ Slides, demos and videos for each lesson

All materials for the course, including the textbook and assignments, are available for free online under a Creative Commons license.

Note: Course has already started but you can start from beginning and access all learning materials.

🔗 Course link: http://data8.org/fa22/

#data_science #datascience #Berkeley #data_analysis

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Online Learning With Amazon

Amazon is now offering these free courses on its online learning platform.
If you get access to any of these courses before the 9th of December, you will have free access to those courses purchased until April 2023.
If you find any of these courses interesting, you can check out other courses for free on their platform before Dec 9.

1) The Elements of Data Science | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link:

2) Data Analytics Fundamentals | Data Analytics (BigData) Online Course | AWS Training & Certification
🔗 Course Link:

3) Math for Machine Learning | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link:

4) Machine Learning for Business Challenges | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link:

5) Linear and Logistic Regression | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link:

6) Machine Learning for Leaders | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link:

7) Data Science Capstone: Real World ML Decisions | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link

8) Computer Vision with GluonCV | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link

#data_science #datascience #Amazon #data_analysis #machine_learning

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500+ AI, Machine learning, Deep learning ,Computer vision, NLP Projects with code

Creator: ashishpatel26
Stars ⭐️: 10.7K
Forked By: 3.2K
GitHub Repo: Link

#data_science #deep_learning #nlp #data_analysis #machine_learning #computer_vision #ai #neural_networks

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One of the most frequent questions I got is how to start with data science and machine learning as a complete beginner, and what skills do you need to have. Do you need to know programming, do you need to know math etc.
Below is my answer I wrote on my discord server, few years ago. It's still relevant and hopefully helpful.

Here are some things you should be familiar with to start your journey as data scientist:

Statistics
You need to have some statistical knowledge, like theory of probability, bayes theorem, probability distributions (uniform, normal/gaussian, logarithmic, exponential, chi-square distribution etc), you should know some basics like what is mean, median and mode. You should understand hypothesis testing and statistical significance as well. If mentioned terms are not familiar to you try researching about them. I shared 4 books of statistics for data science here at discord, they might be useful.

Programming
Generally you are going to need some programming background, which languages have you used before?
Most of people use python, it's great for preparing data as well as using some ML packages for creating machine learning models. What is great about Python is that it's very beginner friendly. R programming language is another option for data science/machine learning. Java and Scala offers nice libraries for data science as well. I personally use Java at my work.

Most important libraries
In case Python is your first choice (and it probably is if you are beginner) then you should check pandas - the biggest library for data manipulation and data analysis, numpy - library for multidimensional arrays and matrices, there are many libraries for machine learning as Keras (Deep learning), Scikit-learn, PyTorch, TensorFlow. Some libraries for data visualization are also important - biggest is matplotlib but there are also Seaborn, Plotly, ggplot, Bokeh...
When it comes to java i use deeplearning4j, ApacheSpark, Apache Hadoop, and bunch of NLP (Natural Processing Libraries) which are not so important now if you are total beginner. We will get you there eventually.


Where to start?
If this sounds like too much for you don't worry, that is just an overview of situation in the field. You don't have to know all those libraries, some basics of Pandas, Numpy and maybe Scikit-learn for beginning is enough.

First thing i have ever read about machine learning which is very important for data science is this medium article:
https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471
It's subtitle is: The world’s easiest introduction to Machine Learning and it's not far form truth. After i read this i understood machine learning as well as data science much better.
Tip: medium allows you to read 3 articles for free per month, but if you open them in incognito mode you have unlimited access to all articles for free smile

After finishing this try researching about other ML concepts like: Types of ML algorithms, classification and regression problems, overfitting/underfitting, model evaluation techniques and measures etc.
I would definitely recommend Andrew Ng's courses on coursera, some of them are available on yt as well.

Once you understand basic concepts, you can dive deeper in data science. Learn about datasets, how to prepare data, how to handle missing values, how to perform feature engineering etc. and try to solve some real world data science problems. I shared 500+ interesting data science projects with source code in post above. I also shared a data science live course by UC Berkeley, Fall 2022. Go check that as well.


Phew 😅 , that was lots of text. I got really tired writing it. But since i get 10-20 of these questions every day, mostly on Instagram and WhatsApp, it's better to have all written in one place. I hope i helped, good luck with your data science journey!

#data_science #datascience #Berkeley
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DIMENSIONALITY REDUCTION
Have you heard of Dimensionality Reduction👀?
If this is your first time😃, then get your seats closer🙂.
It means trimming down data to remove unwanted features👌.

Did this make any sense🤷‍♀️? If it didn't then you must know that whenever you have a very large dataset, It can help you capture the majority of your dataset's information within a few number of features.
Here's one method😃 of Dimensionality Reduction you must know.

It's the Principal Component Analysis (PCA)😎. It gives us the ability to plot multivariate data🤯 in 2 dimensions and works perfectly☺️ in identifying the axis of greatest variance in our dataset.

In this method, we take old sets of variables and convert them into a newer set. The new sets created are called principal components⭐️. There is a trade-off between reducing the number of variables while maintaining the accuracy of your model👍🏼.

The next time you have problems working with very large datasets 🤯, you could try Dimensionality Reduction😉

Graphic SQL reference for data manipulation

Source: https://raw.githubusercontent.com/kelsfarmer/SQL/master/SQL%20Data%20Manipulation%20Language%20Cheat%20Sheet.png

Often the hardest part of solving a machine learning problem can be finding the right estimator for the job.

Different estimators are better suited for different types of data and different problems.

The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data.

Source: Scikit-learn

DIVE INTO DEEP LEARNING ||d2l.ai

Here's an Interactive deep learning book with code, math, and discussions.

Implemented with PyTorch, NumPy/MXNet, and TensorFlow.

Book Link : https://d2l.ai/

GitHub Repo: https://github.com/d2l-ai/d2l-en

Stars: 15.7K

Forks:3.4K

#deep_learning #pyTorch #numPy #MXNet #TensorFlow #neural_networks

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Hey folks, this week's round of our programming quiz league is about data science. Here is the quiz link:
http://www.tg-me.com/QuizBot?start=H4Ow9sU8

Feel free to answer on those 8 short questions and let me know about your placement on final score.

Also to those who celebrate today I wish Merry Christmas 🎄🥳😊