Data science relies on a diverse toolkit to address various stages of the data lifecycle. Key tools include programming languages like Python and R, data visualization tools like Tableau and Power BI, machine learning libraries like Scikit-learn and TensorFlow, and big data technologies such as Hadoop and Spark.
00:04In this lecture, I have a blueprint of a machine learning project.
00:09If you want to create a professional live project,
00:13these are the steps that you follow.
00:17So, I will explain these steps.
00:19And follow our first project set up.
00:22First of all, you need to install the mini conda.
00:26This was the last lecture in the middle of the mini conda.
00:30And I have a reason to do this.
00:32It's about 200 MB.
00:35But the mini conda is installed.
00:39One time step is installed.
00:41Second step is set for life.
00:43The second step is the conda create prefix.
00:47This is the number two command.
00:49I have a thing to tell you.
00:51When you have a mini conda installed,
00:53you can use it.
00:54One time step is an assistant,
00:56and it's called conda.
00:57Then you understand conda.
00:59Now, conda is our is into the tool.
01:04If you have a tool,
01:05we can use it.
01:06Conda, you can use it.
01:07Conda, you can use it.
01:08Now, you can do it.
01:09Conda, you can use it.
01:10Conda is made for the application of the command prompt.
01:12So, you can use it.
01:13Conda, you can use it.
01:14Conda, you can use it.
01:15Command prompt.
01:16Command prompt.
01:17This is the terminal.
01:18I have to open it.
01:20I will be here to you, so I will show you this.
01:23So I will show you, if you are a command, you can understand.
01:28No, don't worry, we use this, I will explain you.
01:32Here I will show you just a skeleton, a blueprint.
01:36So I will tell you this, I will say.
01:38We have...we and we...
01:39This is my practice.
01:41This is my practice.
01:42You have a practice.
01:44I will give you a holistic overview.
01:48If you want to create a project, I need 3 libraries, which I want to call 1, 2, 3. Panda, NumPy, Mitprodlib, which I want to create.
02:03If you want to create a project, I want to create a cycle of machine learning model.
02:09Good, so it has been installed.
02:13When you have a project in camera, first when you have a project, then you can see it.
02:21Then you can see it.
02:24Then you can see it.
02:26If you have a paper, you can imagine it.
02:30You can see it.
02:33You can see it in your work.
02:37Notebook के होने के लिए आपके पास एक library होनी चाहिए जिसका नाम है Jupiter, simple Jupiter, तो फिर उससे पहले हम Jupiter install करेंगे, फिर Jupiter install करने के बाद हम run करेंगे command Jupiter notebook, और फिर Jupiter notebook के उपर हम अपना machine learning model लिखेंगे, and when you are done, you can deactivate the project, ठीक है, और यह basically standard flow है, एक project का, then you can just move to step 3, activate,
03:06और जब आप activate करेंगे, तो फिर आपको यह install करने के जरुवत नहीं पड़ेगी, and then you can just call Jupiter notebook, and then work, ठीक है, और फिर उसको deactivate कर सकते हैं, ठीक है, and then it's totally rinse and repeat, आपने फिर इसके बाद नहीं जाना, again अगर कोई नया project करते हैं, उसके लिए आपको different dependencies or libraries चाहिए, तो of course you can create, यह ए
03:36from Miniconda को install करेंगे, और आने वले सबसीकोंड lectures, बने वले सबसीकोंड lectures में देखेंगे, आपके इनी commands को हम use करके न, एक project को setup करेंगे, और आप देखेंगे किस तरीके से एक professional machine learning project setup किया जाता है,
Be the first to comment