GOOGLE CLOUD FREE MACHINE LEARNING AND AI COURSE

Learn how to implement the latest machine learning and artificial intelligence technology by exploring training on BigQuery, TensorFlow, Cloud Vision, Natural Language API, and more

what you will learn:
👌Big Data & Machine Learning Fundamentals
👌Perform Foundational Data, ML, and AI Tasks in Google Cloud
👌Machine Learning on Google Cloud
👌Advanced Machine Learning with TensorFlow on Google Cloud Platform
👌MLOps (Machine Learning Operations) Fundamentals
👌ML Pipelines on Google Cloud
👌Build and Deploy Machine Learning Solutions on Vertex AI
👌Create Conversational AI Agents with Dialogflow CX

https://cloud.google.com/training/machinelearning-ai

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Data Cleaning Checklist

Data cleaning takes up 80% of the data science workflow. Use this checklist to identify and resolve any quality issues with your data.

Link

I understand data science is not all about programming but, as far as I know, Python comes into play to some extent on this matter. How much should I know about programming to do data science?

This question was asked earlier today by one our community member in our main channel @bigdataspecialist.
I decided to share my answer here since it might be interesting to some of you and I am pretty sure great majority of you haven't even noticed his question/my answer.

TL:DR
Programming is important, but you don't have to be an expert. You just need some basic to intermediate skills to prepare your data (which you are going to use in data science tasks), possibly make some data visualizations to gain insights and at the end to create your machine learning models. These basic skills could probably be gained in a month, especially if you are not complete newbie who has never heard of programming 😅

You can get mentioned skills from this course: https://www.coursera.org/learn/python-data-analysis
Teacher is Christopher Brooks and course is created by University of Michigan.
Note: I know it says its paid one, buy you can apply for financial aid and get course for free. That's how I got this course when I just started learning data science.



Keep in mind that Python is not only programming language which comes to mind when you think about doing data science.
For example, for almost all data science and machine learning tasks, I use Java. It's very specific and usually data scientists don't do that, but platform developed by my company is receiving 4k requests per second, so we need something blazing fast, and Python is pretty slow. That's why we use Java.
But if I am going to test something locally, or I need some easy data preparation or data visualizations, I use Python. Creating charts to gain some insights would be real nightmare with Java. But for you as a beginner Python is probably best choice

Long story short:
If you want it fast and easy, python is way to go
IF you want it very fast (but probably pretty hard to make it work) - Java.
If you want to perform advanced calculations and visualizations - R
If you want to show your visualizations dynamically on some web page, then certain JavaScript libraries like D3.js or chart.js.

Hope this helps.

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Huge collection of Data Science materials
By Harvard

You will find lecture notes/Notebooks for every data science/machine learning topic you heard about 🤯

https://harvard-iacs.github.io/2019-CS109A/pages/materials.html

Understanding and Handling Overfitting

When a machine learning method fits the Training Data really well but makes poor predictions, we say that it is Overfit to the Training Data.

Let's try to undertstand how this occurs and how to handle it,

1) Overfitting in Machine Learning
Link

2) Overfitting and Underfitting With Machine Learning Algorithms
Link

3) Overfitting by IBM
Link

4) The Complete Guide on Overfitting and Underfitting in Machine Learning
Link

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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