Coding is just like the language we use to talk to computers. It's not the skill itself, but rather how do I innovate? How do I build something interesting for my end users?
In a recently leaked recording, AWS CEO told employees that most developers could stop coding once AI takes over, predicting this is likely to happen within 24 months.
Instead of AI replacing developers or expecting a decline in this role, I believe he meant that responsibilities of software developers would be changed significantly by AI.
Being a developer in 2025 may be different from what it was in 2020, Garman, the CEO added.
Meanwhile, Amazon's AI assistant has saved the company $260M & 4,500 developer years of work by remarkably cutting down software upgrade times.
Amazon CEO also confirmed that developers shipped 79% of AI-generated code reviews without changes.
I guess with all the uncertainty, one thing is clear: Ability to quickly adjust and collaborate with AI will be important soft skills more than ever in the of AI.
In a recently leaked recording, AWS CEO told employees that most developers could stop coding once AI takes over, predicting this is likely to happen within 24 months.
Instead of AI replacing developers or expecting a decline in this role, I believe he meant that responsibilities of software developers would be changed significantly by AI.
Being a developer in 2025 may be different from what it was in 2020, Garman, the CEO added.
Meanwhile, Amazon's AI assistant has saved the company $260M & 4,500 developer years of work by remarkably cutting down software upgrade times.
Amazon CEO also confirmed that developers shipped 79% of AI-generated code reviews without changes.
I guess with all the uncertainty, one thing is clear: Ability to quickly adjust and collaborate with AI will be important soft skills more than ever in the of AI.
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👩🏫🧑🏫 PROGRAMMING LANGUAGES YOU SHOULD LEARN TO BECOME.
⚔️[ Web Developer]
⚔️[ Game Developer]
⚔️[ Data Analysis]
⚔️[ Desktop Developer]
⚔️[ Embedded System Program]
⚔️[Mobile Apps Development]
⚔️[ Web Developer]
PHP, C#, JS, JAVA, Python, Ruby⚔️[ Game Developer]
Java, C++, Python, JS, Ruby, C, C#⚔️[ Data Analysis]
R, Matlab, Java, Python⚔️[ Desktop Developer]
Java, C#, C++, Python⚔️[ Embedded System Program]
C, Python, C++ ⚔️[Mobile Apps Development]
Kotlin, Dart, Objective-C, Java, Python, JS, Swift, C#👍7
Artificial Intelligence isn't easy!
It’s the transformative field that enables machines to think, learn, and act autonomously.
To truly excel in Artificial Intelligence, focus on these key areas:
0. Understanding AI Foundations: Learn the core concepts of AI, such as search algorithms, knowledge representation, and logic-based reasoning.
1. Mastering Machine Learning: Deepen your understanding of supervised and unsupervised learning, as well as reinforcement learning for building intelligent systems.
2. Diving into Neural Networks: Understand the architecture and workings of neural networks, including deep learning models, convolutional networks (CNNs), and recurrent networks (RNNs).
3. Working with Natural Language Processing (NLP): Learn how machines interpret human language for tasks like text generation, translation, and sentiment analysis.
4. Reinforcement Learning and Decision Making: Explore how AI learns through interactions with its environment to optimize actions and outcomes, from gaming to robotics.
5. Developing AI Models: Master tools like TensorFlow, PyTorch, and Keras for building, training, and evaluating machine learning and deep learning models.
6. Ethical AI and Bias: Understand the challenges of fairness, transparency, and ethical considerations when developing AI systems.
7. AI in Computer Vision: Dive into image recognition, object detection, and segmentation techniques for enabling machines to "see" and understand the visual world.
8. AI in Robotics: Learn how AI empowers robots to navigate, interact, and make decisions autonomously in the physical world.
9. Staying Updated with AI Trends: The AI landscape evolves quickly—stay on top of new algorithms, research papers, and applications emerging in the field.
AI is about developing systems that think, learn, and adapt in ways that mimic human intelligence.
💡 Embrace the complexity of building intelligent systems that not only solve problems but also innovate and create.
Free Books and Courses to Learn Artificial Intelligence👇👇
Introduction to AI Free Udacity Course
13 AI Tools to improve your productivity
Introduction to Prolog programming for artificial intelligence Free Book
Introduction to AI for Business Free Course
Top Platforms for Building Data Science Portfolio
Artificial Intelligence: Foundations of Computational Agents Free Book
Learn Basics about AI Free Udemy Course
Amazing AI Reverse Image Search
By focusing on these skills, you’ll gain a strong understanding of AI concepts and practical skills in Python, machine learning, and neural networks.
Like for more similar content ❤️
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
#artificialintelligence
It’s the transformative field that enables machines to think, learn, and act autonomously.
To truly excel in Artificial Intelligence, focus on these key areas:
0. Understanding AI Foundations: Learn the core concepts of AI, such as search algorithms, knowledge representation, and logic-based reasoning.
1. Mastering Machine Learning: Deepen your understanding of supervised and unsupervised learning, as well as reinforcement learning for building intelligent systems.
2. Diving into Neural Networks: Understand the architecture and workings of neural networks, including deep learning models, convolutional networks (CNNs), and recurrent networks (RNNs).
3. Working with Natural Language Processing (NLP): Learn how machines interpret human language for tasks like text generation, translation, and sentiment analysis.
4. Reinforcement Learning and Decision Making: Explore how AI learns through interactions with its environment to optimize actions and outcomes, from gaming to robotics.
5. Developing AI Models: Master tools like TensorFlow, PyTorch, and Keras for building, training, and evaluating machine learning and deep learning models.
6. Ethical AI and Bias: Understand the challenges of fairness, transparency, and ethical considerations when developing AI systems.
7. AI in Computer Vision: Dive into image recognition, object detection, and segmentation techniques for enabling machines to "see" and understand the visual world.
8. AI in Robotics: Learn how AI empowers robots to navigate, interact, and make decisions autonomously in the physical world.
9. Staying Updated with AI Trends: The AI landscape evolves quickly—stay on top of new algorithms, research papers, and applications emerging in the field.
AI is about developing systems that think, learn, and adapt in ways that mimic human intelligence.
💡 Embrace the complexity of building intelligent systems that not only solve problems but also innovate and create.
Free Books and Courses to Learn Artificial Intelligence👇👇
Introduction to AI Free Udacity Course
13 AI Tools to improve your productivity
Introduction to Prolog programming for artificial intelligence Free Book
Introduction to AI for Business Free Course
Top Platforms for Building Data Science Portfolio
Artificial Intelligence: Foundations of Computational Agents Free Book
Learn Basics about AI Free Udemy Course
Amazing AI Reverse Image Search
By focusing on these skills, you’ll gain a strong understanding of AI concepts and practical skills in Python, machine learning, and neural networks.
Like for more similar content ❤️
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
#artificialintelligence
👍3
Machine learning powers so many things around us – from recommendation systems to self-driving cars!
But understanding the different types of algorithms can be tricky.
This is a quick and easy guide to the four main categories: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.
𝟏. 𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.
𝐒𝐨𝐦𝐞 𝐜𝐨𝐦𝐦𝐨𝐧 𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:
➡️ Linear Regression – For predicting continuous values, like house prices.
➡️ Logistic Regression – For predicting categories, like spam or not spam.
➡️ Decision Trees – For making decisions in a step-by-step way.
➡️ K-Nearest Neighbors (KNN) – For finding similar data points.
➡️ Random Forests – A collection of decision trees for better accuracy.
➡️ Neural Networks – The foundation of deep learning, mimicking the human brain.
𝟐. 𝐔𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
With unsupervised learning, the model explores patterns in data that doesn’t have any labels. It finds hidden structures or groupings.
𝐒𝐨𝐦𝐞 𝐩𝐨𝐩𝐮𝐥𝐚𝐫 𝐮𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:
➡️ K-Means Clustering – For grouping data into clusters.
➡️ Hierarchical Clustering – For building a tree of clusters.
➡️ Principal Component Analysis (PCA) – For reducing data to its most important parts.
➡️ Autoencoders – For finding simpler representations of data.
𝟑. 𝐒𝐞𝐦𝐢-𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning.
𝐂𝐨𝐦𝐦𝐨𝐧 𝐬𝐞𝐦𝐢-𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:
➡️ Label Propagation – For spreading labels through connected data points.
➡️ Semi-Supervised SVM – For combining labeled and unlabeled data.
➡️ Graph-Based Methods – For using graph structures to improve learning.
𝟒. 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards.
𝐏𝐨𝐩𝐮𝐥𝐚𝐫 𝐫𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:
➡️ Q-Learning – For learning the best actions over time.
➡️ Deep Q-Networks (DQN) – Combining Q-learning with deep learning.
➡️ Policy Gradient Methods – For learning policies directly.
➡️ Proximal Policy Optimization (PPO) – For stable and effective learning.
ENJOY LEARNING 👍👍
But understanding the different types of algorithms can be tricky.
This is a quick and easy guide to the four main categories: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.
𝟏. 𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.
𝐒𝐨𝐦𝐞 𝐜𝐨𝐦𝐦𝐨𝐧 𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:
➡️ Linear Regression – For predicting continuous values, like house prices.
➡️ Logistic Regression – For predicting categories, like spam or not spam.
➡️ Decision Trees – For making decisions in a step-by-step way.
➡️ K-Nearest Neighbors (KNN) – For finding similar data points.
➡️ Random Forests – A collection of decision trees for better accuracy.
➡️ Neural Networks – The foundation of deep learning, mimicking the human brain.
𝟐. 𝐔𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
With unsupervised learning, the model explores patterns in data that doesn’t have any labels. It finds hidden structures or groupings.
𝐒𝐨𝐦𝐞 𝐩𝐨𝐩𝐮𝐥𝐚𝐫 𝐮𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:
➡️ K-Means Clustering – For grouping data into clusters.
➡️ Hierarchical Clustering – For building a tree of clusters.
➡️ Principal Component Analysis (PCA) – For reducing data to its most important parts.
➡️ Autoencoders – For finding simpler representations of data.
𝟑. 𝐒𝐞𝐦𝐢-𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning.
𝐂𝐨𝐦𝐦𝐨𝐧 𝐬𝐞𝐦𝐢-𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:
➡️ Label Propagation – For spreading labels through connected data points.
➡️ Semi-Supervised SVM – For combining labeled and unlabeled data.
➡️ Graph-Based Methods – For using graph structures to improve learning.
𝟒. 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards.
𝐏𝐨𝐩𝐮𝐥𝐚𝐫 𝐫𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:
➡️ Q-Learning – For learning the best actions over time.
➡️ Deep Q-Networks (DQN) – Combining Q-learning with deep learning.
➡️ Policy Gradient Methods – For learning policies directly.
➡️ Proximal Policy Optimization (PPO) – For stable and effective learning.
ENJOY LEARNING 👍👍
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