FREE DATA SCIENCE, MACHINE LEARNING AND DEEP LEARNING COURSES WITH CERTIFICATES

1) Data Science 101
Rating ⭐️: 5.6k+
Duration ⏰: 3 hours on-demand video
Course Link : Enroll Now

2) Deep Learning Fundamentals
Rating ⭐️: 5.6k+
Duration ⏰: 3 hours on-demand video
Course Link : Enroll Now

3) Game-playing AI with Swift for TensorFlow (S4TF)
Rating ⭐️: 20
Duration ⏰:4 hours of on-demand video
Course Link: Enroll Now

4) Introduction to Machine Learning with Sound
Rating ⭐️: 17.1k+
Duration ⏰: 4 hours of on-demand video
Course Link: Enroll Now

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

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TENSORFLOW FREE LEARNING RESOURCES

1) Hello, TensorFlow!
Building and training your first TensorFlow graph from the ground up.
COURSE LINK: Click Me
Source: O reily

2) Intro to TensorFlow for Deep Learning
This course is a practical approach to deep learning for software developers
Course Link: Click Me
Source: Udacity

3) Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Course Link: Click Me
Source: Coursera

4) Get Started With TensorFlow
Course Link: Click Me
Source: TensorFlow

5) Tensorflow | Tensorflow Tutorial For Beginners | Intellipaat
Course Link: Click Me
Source: Intellipaat

6) Practical Machine Learning with Tensorflow
Course is jointly offered by Google and IIT Madras. After this course, the students will be able to build ML models using Tensorflow.
Course Link: Click Me
Source: NPTEL

7) TensorFlow Full Course
Course Link: Click Me
Source: Simplilearn

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

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Data Analysis with Python: Zero to Pandas

Data Analysis with Python: Zero to Pandas" is a practical and beginner-friendly introduction to data analysis covering the basics of Python, Numpy, Pandas, Data Visualization, and Exploratory Data Analysis.

The course is self-paced and there are no deadlines. There are no prerequisites for this course.

👌Watch hands-on coding-focused video tutorials
👌Practice coding with cloud Jupyter notebooks
👌Build an end-to-end real-world course project
👌Earn a verified certificate of accomplishment
👌Interact with a global community of learners


Link:https://jovian.ai/learn/data-analysis-with-python-zero-to-pandas

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Learn PyTorch for Deep Learning

Pytorch is a popular framework for doing Machine Learning in Python. You can use it to build data models, then ask questions of those models. If you're interested in Data Science, and know a bit of Python, this course is a solid place to start your journey. You'll code along at home as you learn about Datasets, Neural Networks, Computer Vision, and more.

Course Link

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

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Practical Statistics for Data Scientist Free Book

Creator: ghoshark
Stars ⭐️: 100
Forked By: 48
GithubRepo: Link

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Udacity Data Science Course
Want to learn what it takes to be a data scientist 🤯? Hop on this course😁.
Not to worry, It is beginner friendly😄.

Here's what you will be learning:

😎Data Manipulation
😎Data Analysis with Statistics and Machine Learning
😎Data Communication with Information Visualization
😎Data at Scale -- Working with Big Data
Course Link: Enroll Now👈

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Fast.AI Practical Deep Learning Course!!!
This super amazing😱 free course😍 is designed for people with some coding experience who want to learn how to apply deep learning and machine learning to practical problems🤩.

Here's what you will be learning from this course😉:
😇Build and train deep learning models for computer vision, natural language processing, tabular analysis, and collaborative filtering problems
😇Create random forests and regression models
😇Deploy models
😇Use PyTorch, the world’s fastest growing deep learning software, plus popular libraries like fastai and Hugging Face

Course Link

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

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Google's Making Friends with Machine Learning Course By Cassie Kozyrkov

This course is an absolute👌 gem⭐️

You can now enjoy all 6.5 hours🤩 of Google’s legendary🤯 AI course designed to enlighten AI beginners, grow technology leaders, inform better citizens, and amuse🤭 AI experts!

Are you as excited😃 as I am?😊
Click This Link

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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😉