Hi, everyone in this video clip, I will show you how to install other libraries for AI and Python coding. We already installed Anaconda 3. So, in order to install other libraries, there are multiple ways you can use, but I'm using "Anaconda Prompt". How can I open "Anaconda Prompt," click "Windows button" and then here's "All apps," then scroll down until you see "Anaconda"Anaconda 3(64-bit)" click it. [00:00:49] Then the options available, well appear and you see here, and you are choosing "Anaconda Prompt" (Anaconda 3)". Then, a black window pops up. This is the "Anaconda Prompt," here's "Anaconda Prompt" you will see at the top here and now, where you are? Ah, you are in the "base" environment; here is "base" and and "C: drive" under "Users" you are "CELT," my username currently "CELT". If I use different computer later, this name could be changed, but as long as it is English, no problem at all. And don't get confused even though username changes. Okay, now we are creating new environment for specific project. We are not working on the "base" environment, but as I said before, what packages are installed in the "base" environment? What I said before is installing Anaconda means installing Python. So, how can we check the libraries available in "base" environment? [Typing] "conda list" [Hit] "enter" This computer takes a bit of seconds. So, here's available the libraries installed initially in the "base" environment. They will be a Python somewhere because all packages are listed here following alphabetical order "p" somewhere Python here "P Y T H O N, Python" here. This version is 3. 8.5 okay and "Pandas is "Pandas" installed "P A N D A S" here "pandas" is installed and basically "NumPy" also is installed, but this is the "base" environment. We are creating another environment "ml_env". So, what is the command? when you want to create a new environment, coding environment, [typing] "conda create -n" and the environment name that you are going to use currently [typing] "ml_env". and then Python information we are going to use [typing] "Python 3.8", and simply "enter", press "enter". Then it goes, right. It goes and it stops somewhere, and asks us "Proceed" yes[y] or no[n]. Definitely, you are clicktyping here yes, so type "Y" and [hit]enter. The installation process now in "ml_env" environment, I'm installing Python 3.8 again, so it looks like duplication. But as I explained already, it is not duplication because depending on the project, you may use a different Python version. So in that case, you create environment for each project and you use a specific Python version, now it is done. And here, it already shows "conda activate ml_env" . If you want to get into that environment. But first let's check available environment [Typing] "conda env list". This is what we already study. Then in the "base" environment, you can check what coding environment exits on your computer. So "base" environment here, right? "base" environment and "ml_env" environment that we created is existing. Now, let's enter "ml_env". So how can we enter? As I already explained [Typing] "conda activate ml_env" [hit]enter. Oh, now the "base" is change it into "ml_env". It means that we successfully created the "ml_env" environment. And now we are in that new environment. So, let's check [Typing] "conda version" here the same version because we're using the same "conda" environment. The current "conda" version is 4.9.2. Then what is the Python version? [typing] "python --version". In this new environment, Python 3.8.8 is installed well okay, so everything is installed. But we need to install additional libraries. First, let's install, deep learning environment. "TensorFlow", how do we install TensorFlow?.[typing] "conda install" so easy [typing] "tensorflow", we are not specifying version information. So, the newest "TensorFlow" will be installed, and we are installing again, "CPU" version, not the "GPU" version, okay. So, simply hit enter, then it takes off a little bit of a time, and you keep going on and either ask you, yes or no, again, probably. Yes or no, it is asking so "yes" and then [hit] enter then all the tools in Python or TensorFlow will be installed. Now, "TensorFlow" installation is done. So, at the top, "Done" appears, and the prompter is blinking, right? well, so now "Tensorflow" is installed, but we need to check where the TensorFlow is correctly installed or not. So, in order to check rather, TensorFlow is working well. First, we need to bring up Python. Python here is simply interpreting. Now, I am beginning to communicate with the computer. So, in order to check whether TensorFlow is working well, I need to create a simple code. Then whether it is correctly implemented or not, I am checking it. Okay. So, in order to use "Tensorflow," we need to first boot Python, interpreter. How can you boot Python interpreter? Typing simply "python" after typing "python" here's "python" prompter is a three greater-than sign. That is the Python prompter this one. So, Python version information is provided 3.8.8. So now the interpreter is ready. It means that between machine and me, there is someone interpreting my language into a machine language that a computer can understand. So, Python is simply placing that role. So now the interpreter is ready. Then I need to also boot "TensorFlow" because I'm going I'm going to use that package. How can I call "TensorFlow"? Simply import I'm in importing "TensorFlow" library in working space, [typing] "import tensorflow as tf" why I'm using this one because after calling "TensorFlow". If I do not give "tf" here simplified name. I'm calling it with abbreviated name "tf" it is because it is easy to use, so [hit] "enter". Then it takes a little bit of time because "TensorFlow" is a big library. Now the computer is bringing "TensorFlow" into working space. Now, the python prompt reappears. It means that "TensorFlow" is ready. And I called "TensorFlow" as a "tf" from now on. Now, let me create an object, a greeting object[typing] "hello" and equal sign [typing] "=" and [typing] "tf" object [typing]".constant". Don't need to worry about this one simply assume this is a checking process. You are going to learn the details about what is constant later. I'm putting in a text language here like a [typing] "Hello" or, [typing]"Hello, Python" or [typing] "Mr. Kwon" whatever you can type [typing]"whatever". You can type and it because this is text; you are putting another quotation and closing the text line with parentheses within these parentheses. This is text or line of text. And that line of text is stored in the "hello" object. Now, we need to print whether it is correctly working. So, "hello" object is created, but sometimes you will see this kind of blah-blah-blah messaging, ignore it is just okay it's okay ignore it, as long as the next step works, it is okay. Then "hello" object is created, then we need to print that object [typing]"print(hello)" So, then the computer will return the message storage in "hello" then [hit] "enter". Oh "tf.Tensor". The object type is "tf.Tensor" information is "Hello Python, blah, blah, blah", or what if is already typed in and shape information and data type string and here's "b'" this is by information. So, this is actually the "print" commander offered by Python. So, using Python "print" command, we are printing an object created within "TensorFlow" So you can use the "TensorFlow" object. You can call the "TensorFlow" object within Python. In that case, all information, detailed information is provided. You don't want to see this kind of detail. I want to see just the text message. In that case., use the "print" command offered by "TensorFlow" in that case, how can I use the print message command offered by "tf"? Simply [typing] "tf.print" it means that "tf.print" you are using "print" function within "TensorFlow". Then "Hello" what happens? Huh? The text message is offered "Hello, Python Mr. Kwon whatever". Yeah. So it means that "TensorFlow" is well installed. You're ready to do Python coding, and you are ready to do AI coding because you already installed it. Now, what is the TensorFlow version? So, in that case, how can you check? [typing] "print" we can use this one, [typing] "tf __version__" and close, right now the "TensorFlow" version installed in this environment is 2.3.0. Now, we check whether TensorFlow is correct or not. Now, we need to get out of Python environment, because even though we cannot see it, Next to me, there is a Python interpreter. How can I close it? [typing] "quit ()" then now Python prompter disappears. And now you see you are in the "ml_env" environment. In order to close but before getting out of this environment, we need to install other packages ; what other packages. There are many other packages. We need to install like a "Numpy," "pandas," "matplotlib". So, I'm going to do that [typing] "conda list numpy pandas matplotlib seaborn" and what scikit-learn [typing] "scikit-learn keras". Those libraries are needed for machine learning and deep learning libraries. So, I type it. Something oh "conda list". I made a mistake is "conda install". I made a wrong typing. So, go back to the command line [typing] "conda install" right and then [hit] "enter". It takes also a little bit of time, and it will ask you, yes or no question again, then again, you simply type "Y" and [hit] enter then all the packages will be automatically installed in your working, coding environment "ml_env". So, now installation is done. Let me check what libraries, are existing in this environment [typing] "conda list" [hit enter].. So let's see, "Tensorflow" is here, "tensorboard" if you learn "TensorFlow," use "Tensorboard" for graphical presentation. You are going to use "Tensorboard" it is also installed. "Seaborn" is here "Scikit-learn" is here, and "Python' is here, and also "NumPy" is here, "Pandas" is here, and "Matplotlib" it is also here, "Keras" is also installed here. So everything is ready. Everything is ready, full coding and AI coding, Python coding, and and AI coding environment is ready. Now we need to get out of the environment. So how can we get out this environment [typing] "conda deactivate" then where you are, I'm getting out of "ml_env" now where I'm "base" environment. Now, I am at base environment. Before closing this Anaconda prompt, we are not going to use Anaconda prompter anymore in coding, Python coding, because we are going to use another interface, which is far easier to code in this, "Anaconda Prompt". Here you need to type everything, so line by line executing. But we are going to use "Jupyter Lab" in the following video clip. I'm going to explain how to install "Jupyter Lab" and how to use Jupyter lab in order to code with Python. So, see you later. But before closing this class, we need to get out of base environment. How do we get out? [typing] "exit" now we are getting out, closing "base" environment also. So, then the anaconda prompt disappears. So, all installation process and creating a coding environment is done. What still remaining? Yeah. Beginning to coding.