In traditional programming, when aiming to implement new functionalities or automate tasks, software development is typically required. This involves writing code containing a predetermined set of instructions, such as if-then-else statements, to direct the computer’s actions. Consequently, to accomplish a variety of tasks, a corresponding number of rules must be provided to the computer, posing a significant challenge. This limitation highlights that conventional programming approaches lack generalizability.
if you haven’t done it by yourself, requires laying out in excruciating detail every single step that you want the computer to do in order to achieve your goal. Now, if you want to do something that you don’t know how to do, then this is going to be a great challenge. Basically, regular programming is pretty limited and can’t make decisions on its own. That’s why we need generalized programming, which is more than just a programmer and can make decisions from our perspective.
So, basically, this Arthur Samuel had a challenge in 1956. He wanted to teach a computer to beat him at checkers. Like, how do you even do that? Well, he came up with a plan. He had the computer play against itself over and over again, like thousands of times, until it learned how to play checkers really well. And guess what? It actually worked! By 1962, the computer had even beaten the Connecticut state champion. Pretty impressive, right?
Even though artificial intelligence, or AI, has only been around for less than a hundred years, the idea of machines that can think goes way back. Even in ancient Greece, people were talking about intelligent robots and artificial beings. The whole idea of AI really starts with asking if machines can think like humans.
1955
In 1955, Allen Newell and Herbert A. Simon made the first computer program meant to act like a smart thinker. They called it the "Logic Theorist." This program tried to prove math ideas using logic symbols. It used a special way of searching for answers that imitated how humans solve problems. [7]. The Logic Theorist was like the first computer tool that could solve lots of different problems, not just one. It was a big deal in the world of smart computer programs. [7].
1- Foundations: Explore basic Python syntax, data types, control structures, functions, and object-oriented programming principles.
2-Data Manipulation and Analysis: Learn how to work with data using popular Python libraries such as NumPy, pandas, and matplotlib for tasks like data cleaning, transformation, aggregation, and visualization.
3- Machine Learning with Python: Dive into the world of machine learning using libraries like scikit-learn and TensorFlow/Keras, covering topics such as regression, classification, clustering, and neural networks.
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| Tutorial | Video | Code |
|---|---|---|
| 🌐1- Setup Environment for Python | 1 | Content 3 |
| 🌐1- What is mean by programming | 1-2 | Content 3 |
| 🌐2- What is Python | 1-2 | Content 6 |
| 🌐3- Python integrated development environment (IDE) | --- | --- |
| 🌐4- Best Free Resources to Learn Python | --- | --- |
| 🌐5- Understanding Variables and Types in Python | Content 2 |  |
| 🌐6-Understanding Operators in Python: A Comprehensive Guide | 1 | |
| 🌐7-Understanding string in Python | Content 2 | |
| 🌐8- Understanding Control Flow in Python | Content 2 | |
| 🌐9- Loops and Iterables | 1-2 | |
| 🌐10-Function | 1-2 | |
| 🌐11- Dictionaries | Content 2 | |
| 🌐11- List | Content 2 | |
| 🌐12-Classes and Objects | Content 2 | |
| 🌐13-Modules | 1 | |
| 🌐14-Packages | 1 | |
| 🌐15-File handling | -- | |
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