Free lessons and articles where you can learn artificial intelligence topics (focused on machine learning and robotics) by working on your own from home!
I created this repository to collect links that I find useful in the field of free road maps and resources for AI. I'll update regularly!
- Basics of Mathematics for Machine Learning
- Linear Algebra
- Linear Algebra Review
- Introduction to Robotics
- Learning from Data
- Deep Learning
- Natural Language Processing with Deep Learning (DDI)
- Full Stack Deep Learning
- Introduction to Baseline: Data, ML, AI
- Data Science with Python
- Google's Python Class
Artificial intelligence is the type of intelligence displayed by machines as opposed to natural intelligence displayed by animals, including humans Specific applications of artificial intelligence include specialized systems, natural language processing, speech recognition, and machine vision.
As the hype around AI accelerates, sellers are scrambling to promote how their products and services use AI. What they often call AI is just one component of AI, like machine learning. AI systems work by combining large datasets with intelligent, iterative processing algorithms to learn features and patterns in the data they analyze Every time an AI system runs a series of data processing, it tests itself and develops additional expertise. None of the programming languages are synonymous with AI, but a few are popular, including Java, R, and Python. In general, AI systems work by ingesting large amounts of selected training data, analyzing the data for patterns and correlations, and using those patterns to make statements about future situations. In this way, a chatbot that feeds samples of text chats can learn to make lifelike exchanges with people, or an image recognition tool can learn to identify and identify objects in images by examining millions of samples. AI programming focuses on three cognitive skills: reasoning, self-correction, and learning. Learning processes. In this aspect, AI programming focuses on collecting data and creating rules on how to turn data into actionable information. Rules, called algorithms, provide computing devices with gradual instructions on how to complete a particular task. Reasoning processes. In this aspect, AI programming focuses on the desired result by choosing the right algorithm. Self-correction processes. In this aspect, AI programming is designed to ensure that algorithms deliver the most accurate results possible by constantly fine-tuning.
AI can give businesses insights into their operations that they may not have been aware of before, and in some cases, AI can perform tasks better than humans. In particular, successive, detail-oriented tasks, such as analyzing a large number of important documents to make sure that the relevant fields are filled in properly, AI tools often perform tasks quickly and with relatively few errors. This opened the door to new business opportunities in productivity. Before the current wave of artificial intelligence, it was impossible to imagine using computer software to connect drivers and taxis, but today Uber has achieved this, becoming one of the largest companies in the world.It uses advanced machine learning algorithms to predict when people will need to take a taxi in certain areas, which helps drivers proactively hit the road before they need to. For example, Google has become one of the largest companies for a range of online services, using machine learning to learn how people use their services and subsequently improving them. In 2017, the company’s CEO, Sundar Pichai, announced that Google would operate as an “AI First” company. Today, the largest and most successful businesses have used artificial intelligence to gain an advantage over their competitors and to improve their operations.
Deep learning artificial intelligence technologies and artificial neural networks are developing rapidly because artificial intelligence processes large amounts of data much faster than humans and makes more accurate predictions than is possible with human capabilities. While the huge volume of data generated will engage a human researcher, artificial intelligence applications that use machine learning can quickly turn that data into action. As of this writing, the primary disadvantage of using AI is that it is expensive to process the large amounts of data required by AI programming.
- Advantages
Good at detail-oriented work
Reduced time for data-heavy tasks
Delivers consistent results and Virtual agents with AI support are always available.
- Disadvantages
Expensive
It requires deep technical expertise
Limited supply of qualified programmers to create AI tools
He only knows what is shown and Lack of the ability to generalize from one task to another.
Strong AI versus weak AI
Artificial intelligence can be categorized as weak or strong. Limited AI, also known as Narrow AI, is an AI system designed and trained to complete a specific task. Industrial robots like Amazon Alexa and virtual personal assistants use weak AI. General AI, also known as artificial general intelligence (AGI), describes programming that can replicate the cognitive abilities of the human brain. A powerful AI system, when presented with an unfamiliar task, can use fuzzy logic to apply information from one domain to another and autonomously find a solution. In theory, a strong AI program should be able to pass both the Turing Test and the Chinese chamber test.
A person’s emotional intelligence determines his or her entire life. It is seen that individuals with high emotional intelligence, that is, people who understand the feelings of both themselves and others and use them correctly, are more successful in life.
In the development of artificial intelligence, the role of high emotional intelligence is great. Artificial intelligence (AI) is a system or machine that can mimic human behavior and learn progressively from the data it receives to perform certain tasks.In modern times, it is true that in most areas of our lives, in our homes, in the office, in our daily routines, we benefit from artificial intelligence products.
When most people say “artificial intelligence,” they think of the intelligent humanoid robots that have taken over the world.
But the reality is that artificial intelligence is not designed to replace humans. Its purpose is to expand the boundaries of human capabilities and opportunities.
Therefore, this technology is a valuable source of business.
The advancement of technology causes the application areas of artificial intelligence to expand day by day.
This is one of the questions that most people think.
“If artificial intelligence takes precedence over emotional intelligence, how will it affect our lives, human psychology?” In every positive and negative situation we experience, we think, question and make decisions based on our emotional intelligence.
If we think about an event we have experienced, we see that our previous experiences, emotions, behaviors, decisions, analyzes come to our aid at that time.
Or we remember the behavior and experience of our friend, someone close, others, and we make our decision accordingly.
So, will the robots trained by artificial intelligence, the algorithms it works with, etc. be able to implement this? It is also seen that the changes that artificial intelligence brings to human life worry some people because they affect the adaptation process. Artificial intelligence creates a model by analyzing the given database and similar examples. Shows the results using this model.
We humans learn about our environment by observing what is happening around us and we make decisions based on our feelings and thoughts. However, as artificial intelligence programs, robots and machines are taught the emotions gathered from these real-life experiences, the areas of application are expanding more.
This limits people’s feedback for what to see. One of the most common problems in recent times is Burnout syndrome.
The fact that people who work long hours without taking a break or resting in their career life or household chores are constantly in stressful situations causes burnout syndrome in them.
Those who experience burnout syndrome form a more pessimistic outlook on life and may feel tired and hopeless all the time.
Since burnout syndrome is more work- and work-stressed, career-induced, in such cases, robots, machines, etc. trained by artificial intelligence make the burnout syndrome less common, people work more efficiently, do not get too tired, and have time for themselves.
And this situation plays a big and important role in human psychology.
Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University, explained in a 2016 article that AI can be categorized into four types, beginning with the task-specific intelligent systems in wide use today and progressing to sentient systems, which do not yet exist. The categories are as follows:
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Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
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Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
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Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
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Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
AI is incorporated into a variety of different types of technology. Here are six examples:
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Automation. When paired with AI technologies, automation tools can expand the volume and types of tasks performed. An example is robotic process automation (RPA), a type of software that automates repetitive, rules-based data processing tasks traditionally done by humans. When combined with machine learning and emerging AI tools, RPA can automate bigger portions of enterprise jobs, enabling RPA's tactical bots to pass along intelligence from AI and respond to process changes.
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Machine learning. This is the science of getting a computer to act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms: Supervised learning. Data sets are labeled so that patterns can be detected and used to label new data sets. Unsupervised learning. Data sets aren't labeled and are sorted according to similarities or differences. Reinforcement learning. Data sets aren't labeled but, after performing an action or several actions, the AI system is given feedback.
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Machine vision. This technology gives a machine the ability to see. Machine vision captures and analyzes visual information using a camera, analog-to-digital conversion and digital signal processing. It is often compared to human eyesight, but machine vision isn't bound by biology and can be programmed to see through walls, for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision, which is focused on machine-based image processing, is often conflated with machine vision.
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Natural language processing (NLP). This is the processing of human language by a computer program. One of the older and best-known examples of NLP is spam detection, which looks at the subject line and text of an email and decides if it's junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis and speech recognition.
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Robotics. This field of engineering focuses on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. For example, robots are used in assembly lines for car production or by NASA to move large objects in space. Researchers are also using machine learning to build robots that can interact in social settings.
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Self-driving cars. Autonomous vehicles use a combination of computer vision, image recognition and deep learning to build automated skill at piloting a vehicle while staying in a given lane and avoiding unexpected obstructions, such as pedestrians.
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Python Libraries for Data Science and Machine Learning:
☑️ Pandas
☑️ Numpy
☑️ Matplotlib
☑️ Seaborn
☑️ Sklearn
☑️ Scipy
☑️ Tensorflow
☑️ Pytorch
☑️ Open CV
☑️ XGBoost
☑️ Langchain