Course Content
Pre-requisite
To learn from this course, please finish the pre requisites.
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Pre-requisite for this course
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Introduction to Artificial Intelligence (AI)
This section will lay down you understanding about AI
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What is Artificial Intelligence?
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History of Artificial Intelligence
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Big players in AI
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AI in our Daily life
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How to get a job in AI
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Will me job be replaced by AI?
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Main Goals of this course
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Main Goals of AI ka Chilla
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Software for this course
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Why Do you need to learn pyhton for AI?
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Software installation and websites for this course
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Conda Environments (A-Z)
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VScode setup for python for AI and Data Science
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Python-101 (Complete for AI and Data Science)
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Important Lecture before the next ones.
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First Line of code in python
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Variables in Python
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Operator in python
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Data Types in Python
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Indentations and if conditions in Python
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User input program in python
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Data structures and indexing in python
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Control flow statements in python
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Nested Loops in Python
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Functions & Lmbda funtions in Python
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Modules and Libraries in Python
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Types of Errors in Python
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Jupyter Notebook and File Handling in Python
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MarkDown Crash Course for using jupyter notebooks
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How Data is fueling AI?
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Importance of Data in AI
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Data and AI ka jor
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ABC of Statistics
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ABC of Statistics (Part-1)
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ABC of Statistics (Part-2)
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ABC of Statistics (Part-3)
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ABC of Statistics (Part-4)
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ABC of Statistics (Part-5)
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ABC of Statistics (Part-6)
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ABC of Statistics (Part-7)
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ABC of Statistics (Part-8)
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ABC of Statistics (Part-9)
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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.
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Introduction to EDA and Python coding
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Important Terminoogies in EDA
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Why and how to deal missing values?
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Missing Value Imputation in Python
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Complete A-Z EDA Project
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Automatic EDA
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AutoViz Library
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Use Ai for Fast paced EDA
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Big Datasets for EDA practice
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EDA on Apple Store Apps (Part-1)
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EDA on Apple Store Apps (Part-2)
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Complete EDA on Apple APP store Data
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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.
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Introduction to streamlit and your first web app
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Important logins for upcoming lectures
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Introduction to API
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Important APIs
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Apna chatGPT bnayen
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Introduction to LangChain
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Ask the text app (Complete Project)
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Ask the docx, pdf, txt file app- Advanced (Complete Project)
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Other important APIs for the app development
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Large Language Models (LLMs)
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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.
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Introduction to Machine Learning
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Machine Learning and its types
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Regrssion vs. Classification and Types of Algorithms in Machine Learning
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Metrics of Regression Evaluation
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Missing values imputation for Data pre-processing (Part-1)
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Missing values imputation for Data pre-processing (Part-2)
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Simple- and Multi- Linear Regression
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Multiple Linear Regression and Types of Data Encoding
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Evaluating Regression Models – Must-Know Metrics Explained
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Regression vs. Classification
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Features vs Labels – Explaining the Core Machine Learning Data Components
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Sentiment Analysis Web App
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Evaluation Metrics for Classification models in Machine Learning
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Prompt Engineering to make a complete webapp in less than 13 minutes
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EDA using prompt engineering
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Classification Models in sk-learn (Logistic Regression)
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Regression vs. Classification
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Logistic Regression (Theory)
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Logistic Regression (Coding in Python)
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Evaluation Metrics for Classification
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Support vector Machines (Part-1)
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Support vector Machines (Part-2)
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Machine Learnig Types and algorithms defined in one liner
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Naive Bayes Algorithm
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Cross Validation
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K-Nearest Neighbours (KNN)
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Mathematics behind K-Nearest Neighbours Algorithm
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Best Model Selection out of many
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How to use github code?
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HyperParameter Tuning
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Selecting best Hyperparamter Tuned Model
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Pipelines in Machine Learning
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Ensemble Methods in Machine Learning ALgorithms
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Decision Tree Algorithm
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ADAboosting Algorithm
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Random Forest Algorithm`
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XGBoost, CATBoost, and lightGBM Algorithm
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Lasso and Ridge Regression | L1 and L2 Regularization
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Ensemble Algorithms in Python with coding
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Unsupervised Machine Learning Algorithms
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Clustering Algorithms
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K-means Clustering
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kmeans vs. kmeans++ Clustering
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Hierarchical Clustering Theory
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Hierarchical Clustering Practice in Python
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DBSCAN (Density-based spatial clustering of applications with noise)
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DBSCAN vs. OPTICS
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Gaussian Mixture Models (GMMs)
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Evaluation metrics for GMMs
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Feature Engineering (Part-1)
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Feature Engineering (Part-2)
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Principal Component Analysis (PCA) – (Part-1)
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Principal Component Analysis (PCA) – (Part-2)
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SVD (Singular Value Decomposition)
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tSNE (t-distributed Stochastic Neighbor Embedding)
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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.
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Prompt Engineering Crash course
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Using Prompt Engineering to Craft Powerful Prompts with AI
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Blogging Tips and Tricks using Prompt Engineering
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Website Development Using Hostinger AI prompting
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What we have learned so far?
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What we have learned so far?
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Software Development
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Develop Software to do EDA in Python
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Online Earning
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How to earn more using AI tools
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Machine Learning one stop shop (Complete Project)
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Full Stack ML in Python
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How to use Google Colab?
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Google Colab use, tips and tricks
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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.
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Introduction to Deep Learning
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What is Neural Network? and How we can construct it
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Number of neurons in Each Layer
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Computer Vision (Basics)
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Deep Learning and Computer Vision
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Activation Functions (Part-1)
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Activation Functions (Part-2)
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Activation Functions (Part-3)
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How to choose an activation function?
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Artificial Neural Network (Basic Network Structure)
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ANN Advance Theory with Concepts
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Regression and call back function
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Binary and Multi-class classification
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Training and Validation loss
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Convulutional Neural Network (CNN)- (Part-1)
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Convolutional Neural Network (CNN) Theory -(Part-2)
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Convolutional Neural Network (CNN) Practice in Python-(Part-3)
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Master Convolutional Neural Network (CNN) – (Part-4)
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Recurrent Neural Network (RNN), LSTM and GRU in Python
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Recurrent Neural Network (RNN), Time Series and NLP
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15 Important Key terms for NLP and prompting
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WebApp deployment
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Important Logins and websites
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Web App development and Deployment using prompt engineering, chatGPT, github and streamlit cloud
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All about git and gitHub
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git, gitTools and gitHub
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Revision workshop
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Recent Revision and HandsOn Workshop
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Earn Online
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How to earn Money online using AI and Data Science Skills?
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FeedBack
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Your Feedback Matters
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AI ka Chilla 2023- (Complete Artificial Intelligence Course in 40 Days)
    Join the conversation
    FAISAL SIKANDER 2 months ago
    SalamAmmar bhai apka koi contact number nahi hay sir mujai apsay call pay ya whatsapp pay bat karni thi...
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    Hashim Darya 7 months ago
    ada Sir, Jazak ALLAH
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    Muhammad Rizwan Shoukat 7 months ago
    Ada Sir, Brilliant 👍👍👍
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    Muhammad Faizan Khan 8 months ago
    ada
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    Ilya Haider 8 months ago
    ada
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    Muhammad Haroon 8 months ago
    It is informative intro
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