Course Content
To learn from this course, please finish the pre requisites.
Main Goals of this course
Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA) is the process of analyzing data to gain insights and understanding of the data set. This process can help uncover relationships, patterns, and trends in the data, which can lead to better decision making. In Python, EDA can be done using various libraries such as pandas, Matplotlib, and Seaborn. With pandas, you can analyze and manipulate datasets with its built-in functions and tools. Matplotlib and Seaborn allow you to visualize the data in different ways, such as scatter plots, bar graphs, and histograms. Additionally, Python also provides packages such as SciPy and StatsModels for performing statistical analysis, which can be used to test hypotheses and build machine learning models. With the vast amount of tools available in Python, EDA can be performed quickly and efficiently.
Web Application development and Deployment
Web application development is the process of creating web-based applications that can be accessed through the internet. It involves the development of a web browser-based interface that can be used to interact with a web server. Web application development involves the use of technologies such as HTML, CSS, JavaScript, and related frameworks. It can also involve the use of databases and other web technologies. Once the web application has been developed, it must be deployed on a web server, which is responsible for serving the application to users. Deployment involves the configuration of the web server, configuring the application, deploying the code on the server, and ensuring that the application is working properly. Web application deployment also involves security measures such as firewalls, encryption, and authentication. After deployment, the application must be maintained and monitored for any errors or performance issues.
Machine Learning in desi style
Machine Learning in Desi Style is a way to learn the fundamentals of Machine Learning in a more fun and creative way. This involves using Indian languages and culture to help explain important concepts in Machine Learning. This style of learning can be useful for those who are new to the field as it can break down complex concepts into simpler terms. It can also be fun as it allows for students to explore their own cultural context when learning. The use of Indian languages can provide a unique perspective on the topics and allow students to gain insight into Machine Learning in a more personal way.
Prompt Engineering
Prompt engineering is the process of designing and creating prompts for user-interfaces that help guide users to complete tasks. This process involves selecting appropriate prompts, designing the messages and the visuals associated with them, and also testing them to ensure they are effective. Prompt engineering is important because it allows developers to create user-interfaces that are intuitive and user-friendly. This helps users to quickly understand and complete tasks in an efficient manner.Prompt engineering often involves using best practices for designing effective prompts. This includes considering the user's context and goal, ensuring the language used is appropriate for the target audience, and making sure the visuals used are clear and understandable. Additionally, the process also involves testing the prompts with users to ensure they are effective and don't cause any confusion or frustration.Overall, prompt engineering is a critical part of UI design. It allows developers to create user-interfaces that are intuitive, user-friendly, and effective. It also helps to ensure that users are able to complete tasks in an efficient manner. Ultimately, prompt engineering can help to improve user experience and increase user satisfaction.
What we have learned so far?
Machine Learning one stop shop (Complete Project)
How to use Google Colab?
Deep Learning in Desi Andaz
Deep Learning is a type of Artificial Intelligence (AI) that uses multiple layers of neural networks to learn from large amounts of data. It is capable of learning complex patterns and making decisions based on those patterns. Deep Learning models are trained by using large datasets that contain millions of data points. The models are then able to recognize patterns and make decisions or predictions about new data. Deep Learning is used in a variety of applications such as computer vision, natural language processing, and robotics. It is an important component of AI and has enabled computers to become more intelligent and perform tasks that were previously impossible.
All about git and gitHub
AI ka Chilla 2023- (Complete Artificial Intelligence Course in 40 Days)
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    Hashim Darya 4 months ago
    ada Sir, Jazak ALLAH
    Muhammad Rizwan Shoukat 5 months ago
    Ada Sir, Brilliant 👍👍👍
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    Muhammad Haroon 5 months ago
    It is informative intro
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