AI is the next biggest skill to learn.
AI experts are earing up to $200000+ per year.
Here are 4 FREE courses from Google and Microsoft that most people don't know:
https://microsoft.github.io/AI-For-Beginners/?
https://www.cloudskillsboost.google/paths/118
https://www.deeplearning.ai/courses/ai-for-everyone/
https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
More free resources: https://t.me/udacityfreecourse
AI experts are earing up to $200000+ per year.
Here are 4 FREE courses from Google and Microsoft that most people don't know:
https://microsoft.github.io/AI-For-Beginners/?
https://www.cloudskillsboost.google/paths/118
https://www.deeplearning.ai/courses/ai-for-everyone/
https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
More free resources: https://t.me/udacityfreecourse
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Future Trends in Artificial Intelligence 👇👇
1. AI in healthcare: With the increasing demand for personalized medicine and precision healthcare, AI is expected to play a crucial role in analyzing large amounts of medical data to diagnose diseases, develop treatment plans, and predict patient outcomes.
2. AI in finance: AI-powered solutions are expected to revolutionize the financial industry by improving fraud detection, risk assessment, and customer service. Robo-advisors and algorithmic trading are also likely to become more prevalent.
3. AI in autonomous vehicles: The development of self-driving cars and other autonomous vehicles will rely heavily on AI technologies such as computer vision, natural language processing, and machine learning to navigate and make decisions in real-time.
4. AI in manufacturing: The use of AI and robotics in manufacturing processes is expected to increase efficiency, reduce errors, and enable the automation of complex tasks.
5. AI in customer service: Chatbots and virtual assistants powered by AI are anticipated to become more sophisticated, providing personalized and efficient customer support across various industries.
6. AI in agriculture: AI technologies can be used to optimize crop yields, monitor plant health, and automate farming processes, contributing to sustainable and efficient agricultural practices.
7. AI in cybersecurity: As cyber threats continue to evolve, AI-powered solutions will be crucial for detecting and responding to security breaches in real-time, as well as predicting and preventing future attacks.
Like for more ❤️
Artificial Intelligence
1. AI in healthcare: With the increasing demand for personalized medicine and precision healthcare, AI is expected to play a crucial role in analyzing large amounts of medical data to diagnose diseases, develop treatment plans, and predict patient outcomes.
2. AI in finance: AI-powered solutions are expected to revolutionize the financial industry by improving fraud detection, risk assessment, and customer service. Robo-advisors and algorithmic trading are also likely to become more prevalent.
3. AI in autonomous vehicles: The development of self-driving cars and other autonomous vehicles will rely heavily on AI technologies such as computer vision, natural language processing, and machine learning to navigate and make decisions in real-time.
4. AI in manufacturing: The use of AI and robotics in manufacturing processes is expected to increase efficiency, reduce errors, and enable the automation of complex tasks.
5. AI in customer service: Chatbots and virtual assistants powered by AI are anticipated to become more sophisticated, providing personalized and efficient customer support across various industries.
6. AI in agriculture: AI technologies can be used to optimize crop yields, monitor plant health, and automate farming processes, contributing to sustainable and efficient agricultural practices.
7. AI in cybersecurity: As cyber threats continue to evolve, AI-powered solutions will be crucial for detecting and responding to security breaches in real-time, as well as predicting and preventing future attacks.
Like for more ❤️
Artificial Intelligence
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The first channel in the world of Telegram is dedicated to helping students and programmers of artificial intelligence, machine learning and data science in obtaining data sets for their research.
https://t.me/DataPortfolio
https://t.me/DataPortfolio
Telegram
Data Science Portfolio - Kaggle Datasets & AI Projects | Artificial Intelligence
Free Datasets For Data Science Projects & Portfolio
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For Promotions/ads: @coderfun
Buy ads: https://telega.io/c/DataPortfolio
For Promotions/ads: @coderfun
How do you start AI and ML ?
Where do you go to learn these skills? What courses are the best?
There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.
What’s more important than how you start is why you start.
Start with why.
Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.
Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.
Got a why? Good. Time for some hard skills.
I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course
You can completed courses from (in order):
Treehouse / youtube( free) - Introduction to Python
Udacity - Deep Learning & AI Nanodegree
fast.ai - Part 1and Part 2
They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.
Join for more: https://t.me/machinelearning_deeplearning
👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5
Like for more ❤️
All the best 👍👍
Where do you go to learn these skills? What courses are the best?
There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.
What’s more important than how you start is why you start.
Start with why.
Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.
Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.
Got a why? Good. Time for some hard skills.
I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course
You can completed courses from (in order):
Treehouse / youtube( free) - Introduction to Python
Udacity - Deep Learning & AI Nanodegree
fast.ai - Part 1and Part 2
They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.
Join for more: https://t.me/machinelearning_deeplearning
👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5
Like for more ❤️
All the best 👍👍
👍11❤5🥰1
jason-brownlee-machine-learning-mastery-with-python-2016.pdf
2.4 MB
📚 Machine Learning Mastery with Python
Jason Brownlee, 2016
Jason Brownlee, 2016
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Any person learning deep learning or artificial intelligence in particular, know that there are ultimately two paths that they can go:
1. Computer vision
2. Natural language processing.
I outlined a roadmap for computer vision I believe many beginners will find helpful.
Artificial Intelligence 👇👇
1. Computer vision
2. Natural language processing.
I outlined a roadmap for computer vision I believe many beginners will find helpful.
Artificial Intelligence 👇👇
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Before we start, what is computer vision and what do computer vision engineers do?
Computer vision is a field of AI that enables machines to interpret and understand visual data from the world, such as images and videos.
Computer vision engineers develop algorithms and systems to automate tasks like image classification, object detection, and image segmentation, transforming visual data into actionable insights for various applications including healthcare, autonomous driving, and security.
Artificial Intelligence
Computer vision is a field of AI that enables machines to interpret and understand visual data from the world, such as images and videos.
Computer vision engineers develop algorithms and systems to automate tasks like image classification, object detection, and image segmentation, transforming visual data into actionable insights for various applications including healthcare, autonomous driving, and security.
Artificial Intelligence
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Introduction to Computer Vision:
Understanding images and pixels.
Grayscale and color images.
Basic image processing operations.
Image formats and conversions.
Mathematics for Computer Vision:
Linear algebra (matrices, vectors, transformations).
Calculus (derivatives, gradients).
Probability and statistics (distributions, Bayes theorem).
Fourier transforms and convolutions.
Eigenvalues and eigenvectors.
Artificial Intelligence
Understanding images and pixels.
Grayscale and color images.
Basic image processing operations.
Image formats and conversions.
Mathematics for Computer Vision:
Linear algebra (matrices, vectors, transformations).
Calculus (derivatives, gradients).
Probability and statistics (distributions, Bayes theorem).
Fourier transforms and convolutions.
Eigenvalues and eigenvectors.
Artificial Intelligence
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Step 2: Basic Image Processing
Image Manipulation with OpenCV:
Reading, displaying, and saving images.
Basic operations (resizing, cropping, rotating).
Image filtering (blurring, sharpening, edge detection).
Handling image channels and color spaces.
Image Manipulation with PIL and Scikit-Image:
Image enhancement techniques.
Histogram equalization.
Geometric transformations.
Image segmentation (thresholding, watershed).
Artificial Intelligence
Image Manipulation with OpenCV:
Reading, displaying, and saving images.
Basic operations (resizing, cropping, rotating).
Image filtering (blurring, sharpening, edge detection).
Handling image channels and color spaces.
Image Manipulation with PIL and Scikit-Image:
Image enhancement techniques.
Histogram equalization.
Geometric transformations.
Image segmentation (thresholding, watershed).
Artificial Intelligence
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Step 3: Feature Extraction
Traditional Feature Detectors:
Edge detection (Sobel, Canny).
Corner detection (Harris, Shi-Tomasi).
Blob detection (LoG, DoG).
SIFT and SURF features.
ORB features.
Image Segmentation:
Thresholding.
Watershed algorithm.
Contours and shape detection.
Region growing.
Graph-based segmentation.
Artificial Intelligence
Traditional Feature Detectors:
Edge detection (Sobel, Canny).
Corner detection (Harris, Shi-Tomasi).
Blob detection (LoG, DoG).
SIFT and SURF features.
ORB features.
Image Segmentation:
Thresholding.
Watershed algorithm.
Contours and shape detection.
Region growing.
Graph-based segmentation.
Artificial Intelligence
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Step 4: Machine Learning for Computer Vision
Classical Machine Learning Techniques:
K-Nearest Neighbors (KNN).
Support Vector Machines (SVM).
Decision Trees and Random Forests.
Naive Bayes.
Clustering (K-means, DBSCAN).
Dimensionality Reduction:
Principal Component Analysis (PCA).
Linear Discriminant Analysis (LDA).
t-SNE (t-Distributed Stochastic Neighbor Embedding).
Independent Component Analysis (ICA).
Feature selection techniques.
Artificial Intelligence
Classical Machine Learning Techniques:
K-Nearest Neighbors (KNN).
Support Vector Machines (SVM).
Decision Trees and Random Forests.
Naive Bayes.
Clustering (K-means, DBSCAN).
Dimensionality Reduction:
Principal Component Analysis (PCA).
Linear Discriminant Analysis (LDA).
t-SNE (t-Distributed Stochastic Neighbor Embedding).
Independent Component Analysis (ICA).
Feature selection techniques.
Artificial Intelligence
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Step 5: Deep Learning for Computer Vision
Convolutional Neural Networks (CNNs):
Convolutional layers.
Pooling layers.
Fully connected layers.
Activation functions (ReLU, Sigmoid, Tanh).
Batch normalization and dropout.
Advanced CNN Architectures:
AlexNet and VGGNet.
ResNet (Residual Networks).
Inception and GoogLeNet.
DenseNet (Densely Connected Networks).
MobileNet and EfficientNet.
Artificial Intelligence
Convolutional Neural Networks (CNNs):
Convolutional layers.
Pooling layers.
Fully connected layers.
Activation functions (ReLU, Sigmoid, Tanh).
Batch normalization and dropout.
Advanced CNN Architectures:
AlexNet and VGGNet.
ResNet (Residual Networks).
Inception and GoogLeNet.
DenseNet (Densely Connected Networks).
MobileNet and EfficientNet.
Artificial Intelligence
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Step 6: Advanced Topics in Computer Vision
Object Detection:
Region-based methods (R-CNN, Fast R-CNN, Faster R-CNN).
YOLO (You Only Look Once).
SSD (Single Shot MultiBox Detector).
RetinaNet.
Anchor boxes and non-maximum suppression.
Image Segmentation:
Semantic segmentation (U-Net, SegNet).
Instance segmentation (Mask R-CNN).
Panoptic segmentation.
Fully Convolutional Networks (FCNs).
CRFs (Conditional Random Fields).
Artificial Intelligence
Object Detection:
Region-based methods (R-CNN, Fast R-CNN, Faster R-CNN).
YOLO (You Only Look Once).
SSD (Single Shot MultiBox Detector).
RetinaNet.
Anchor boxes and non-maximum suppression.
Image Segmentation:
Semantic segmentation (U-Net, SegNet).
Instance segmentation (Mask R-CNN).
Panoptic segmentation.
Fully Convolutional Networks (FCNs).
CRFs (Conditional Random Fields).
Artificial Intelligence
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Artificial Intelligence for Learning.pdf
2.8 MB
Artificial Intelligence for Learning
Donald Clark, 2024
Donald Clark, 2024
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Masato_Hagiwara_Real_World_Natural_Language_Processing_Practical.pdf
11.5 MB
Real-World Natural Language Processing
Masato Hagiwara, 2021
Masato Hagiwara, 2021
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Step 7: Generative Models
Variational Autoencoders (VAEs):
Encoder and decoder networks.
Latent space representation.
Reparameterization trick.
KL divergence loss.
Applications (data generation, anomaly detection).
Generative Adversarial Networks (GANs):
Generator and discriminator networks.
Adversarial training.
Loss functions (minimax, Wasserstein).
DCGANs (Deep Convolutional GANs).
Applications (image generation, style transfer).
Artificial Intelligence
Variational Autoencoders (VAEs):
Encoder and decoder networks.
Latent space representation.
Reparameterization trick.
KL divergence loss.
Applications (data generation, anomaly detection).
Generative Adversarial Networks (GANs):
Generator and discriminator networks.
Adversarial training.
Loss functions (minimax, Wasserstein).
DCGANs (Deep Convolutional GANs).
Applications (image generation, style transfer).
Artificial Intelligence
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Step 8: Practical Applications and Projects
Identifying Real-World Problems:
Planning and Outlining Projects:
Choosing the Right Algorithm:
Overcoming Overfitting:
Building a Strong Portfolio
Artificial Intelligence
Identifying Real-World Problems:
Planning and Outlining Projects:
Choosing the Right Algorithm:
Overcoming Overfitting:
Building a Strong Portfolio
Artificial Intelligence
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Step 9: Career and Freelance Tips (1/2)
Searching for Internships and Jobs:
Using job portals and company websites.
Leveraging university career centers.
Joining professional associations.
Networking and referrals.
Crafting effective resumes and cover letters.
(2/2)
Preparing for Interviews:
Technical preparation (coding practice, concepts).
Behavioral preparation (STAR method).
Researching companies.
Mock interviews.
Useful apps for interview preparation.
Working with Freelance:
Finding opportunities on freelance platforms.
Building a strong profile and portfolio.
Managing projects and client communication.
Time management and payment methods.
Artificial Intelligence
Hope this helps you ☺️
Searching for Internships and Jobs:
Using job portals and company websites.
Leveraging university career centers.
Joining professional associations.
Networking and referrals.
Crafting effective resumes and cover letters.
(2/2)
Preparing for Interviews:
Technical preparation (coding practice, concepts).
Behavioral preparation (STAR method).
Researching companies.
Mock interviews.
Useful apps for interview preparation.
Working with Freelance:
Finding opportunities on freelance platforms.
Building a strong profile and portfolio.
Managing projects and client communication.
Time management and payment methods.
Artificial Intelligence
Hope this helps you ☺️
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Save significant time every day with these ChatGPT's 7 prompts:
1. Make Hard Topics Easier to Understand:
Prompt:
Divide the (topic) into smaller, simpler pieces.
Use comparisons and examples from everyday life to make the idea easier to grasp and more relevant.
More here 👇👇
ChatGPT Prompts
1. Make Hard Topics Easier to Understand:
Prompt:
Divide the (topic) into smaller, simpler pieces.
Use comparisons and examples from everyday life to make the idea easier to grasp and more relevant.
More here 👇👇
ChatGPT Prompts
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For working professionals willing to pivot their careers to AI:
Here are the steps you can take right now:
1. Learn the basics of AI
==================
You need to understand the differences among various AI jargons (e.g., what is the difference between statistical ML vs. deep learning? What exactly is an LLM?) and when to use which to solve a given business problem. Many fast-paced courses can teach you all of this without having to learn coding. (Shameless plug: I have a course that I will add in the comments section below)
2. Build an AI project in your current work
==============================
Find a problem statement in your current work that can be solved using AI and will deliver some value. Work on this during your extra hours, then showcase it to your management to get official approval to make it a full-fledged project.
3. Collaborate with the AI team in your company for inner sourcing
================================================
Many companies have the concept of inner sourcing where, say, an AI team is too busy and has a list of tasks they have opened on their GitHub repository that others can work on. Use this as an opportunity to do some real AI work and build rapport with the AI team.
4. Attend AI conferences
==================
By attending AI conferences, you will not only learn but also build a network with AI professionals who will help you in your AI career journey.
5. Attend an AI bootcamp at a university or online learning company
=================================================
Artificial Intelligence
👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5
Like for more ❤️
All the best 👍👍
Here are the steps you can take right now:
1. Learn the basics of AI
==================
You need to understand the differences among various AI jargons (e.g., what is the difference between statistical ML vs. deep learning? What exactly is an LLM?) and when to use which to solve a given business problem. Many fast-paced courses can teach you all of this without having to learn coding. (Shameless plug: I have a course that I will add in the comments section below)
2. Build an AI project in your current work
==============================
Find a problem statement in your current work that can be solved using AI and will deliver some value. Work on this during your extra hours, then showcase it to your management to get official approval to make it a full-fledged project.
3. Collaborate with the AI team in your company for inner sourcing
================================================
Many companies have the concept of inner sourcing where, say, an AI team is too busy and has a list of tasks they have opened on their GitHub repository that others can work on. Use this as an opportunity to do some real AI work and build rapport with the AI team.
4. Attend AI conferences
==================
By attending AI conferences, you will not only learn but also build a network with AI professionals who will help you in your AI career journey.
5. Attend an AI bootcamp at a university or online learning company
=================================================
Artificial Intelligence
👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5
Like for more ❤️
All the best 👍👍
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