Course Content
Pre-requisite
To learn from this course, please finish the pre requisites.
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Pre-requisite for this course
06:08
Introduction to Artificial Intelligence (AI)
This section will lay down you understanding about AI
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What is Artificial Intelligence?
31:33
History of Artificial Intelligence
08:11
Big players in AI
08:54
AI in our Daily life
19:07
How to get a job in AI
56:23
Will me job be replaced by AI?
07:42
Main Goals of this course
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Main Goals of AI ka Chilla
12:28
Software for this course
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Why Do you need to learn pyhton for AI?
11:41
Software installation and websites for this course
23:57
Conda Environments (A-Z)
36:48
VScode setup for python for AI and Data Science
24:06
Python-101 (Complete for AI and Data Science)
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Important Lecture before the next ones.
24:06
First Line of code in python
29:43
Variables in Python
13:04
Operator in python
24:00
Data Types in Python
11:32
Indentations and if conditions in Python
06:40
User input program in python
16:57
Data structures and indexing in python
21:42
Control flow statements in python
00:00
Nested Loops in Python
00:00
Functions & Lmbda funtions in Python
29:18
Modules and Libraries in Python
00:00
Types of Errors in Python
00:00
Jupyter Notebook and File Handling in Python
00:00
MarkDown Crash Course for using jupyter notebooks
00:00
How Data is fueling AI?
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Importance of Data in AI
00:00
Data and AI ka jor
00:00
ABC of Statistics
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ABC of Statistics (Part-1)
00:00
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)
00:00
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
35:50
Important Terminoogies in EDA
00:00
Why and how to deal missing values?
00:00
Missing Value Imputation in Python
00:00
Complete A-Z EDA Project
00:00
Automatic EDA
00:00
AutoViz Library
00:00
Use Ai for Fast paced EDA
00:00
Big Datasets for EDA practice
00:00
EDA on Apple Store Apps (Part-1)
00:00
EDA on Apple Store Apps (Part-2)
00:00
Complete EDA on Apple APP store Data
00:00
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
00:00
Important logins for upcoming lectures
00:00
Introduction to API
00:00
Important APIs
00:00
Apna chatGPT bnayen
00:00
Introduction to LangChain
00:00
Ask the text app (Complete Project)
00:00
Ask the docx, pdf, txt file app- Advanced (Complete Project)
00:00
Other important APIs for the app development
00:00
Large Language Models (LLMs)
00:00
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
00:00
Machine Learning and its types
00:00
Regrssion vs. Classification and Types of Algorithms in Machine Learning
00:00
Metrics of Regression Evaluation
00:00
Missing values imputation for Data pre-processing (Part-1)
00:00
Missing values imputation for Data pre-processing (Part-2)
00:00
Simple- and Multi- Linear Regression
00:00
Multiple Linear Regression and Types of Data Encoding
00:00
Evaluating Regression Models – Must-Know Metrics Explained
00:00
Regression vs. Classification
00:00
Features vs Labels – Explaining the Core Machine Learning Data Components
00:00
Sentiment Analysis Web App
00:00
Evaluation Metrics for Classification models in Machine Learning
00:00
Prompt Engineering to make a complete webapp in less than 13 minutes
00:00
EDA using prompt engineering
00:00
Classification Models in sk-learn (Logistic Regression)
00:00
Regression vs. Classification
00:00
Logistic Regression (Theory)
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Logistic Regression (Coding in Python)
00:00
Evaluation Metrics for Classification
00:00
Support vector Machines (Part-1)
00:00
Support vector Machines (Part-2)
00:00
Machine Learnig Types and algorithms defined in one liner
00:00
Naive Bayes Algorithm
00:00
Cross Validation
00:00
K-Nearest Neighbours (KNN)
00:00
Mathematics behind K-Nearest Neighbours Algorithm
00:00
Best Model Selection out of many
00:00
How to use github code?
00:00
HyperParameter Tuning
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Selecting best Hyperparamter Tuned Model
00:00
Pipelines in Machine Learning
00:00
Ensemble Methods in Machine Learning ALgorithms
00:00
Decision Tree Algorithm
00:00
ADAboosting Algorithm
00:00
Random Forest Algorithm`
00:00
XGBoost, CATBoost, and lightGBM Algorithm
00:00
Lasso and Ridge Regression | L1 and L2 Regularization
00:00
Ensemble Algorithms in Python with coding
00:00
Unsupervised Machine Learning Algorithms
00:00
Clustering Algorithms
00:00
K-means Clustering
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kmeans vs. kmeans++ Clustering
00:00
Hierarchical Clustering Theory
00:00
Hierarchical Clustering Practice in Python
00:00
DBSCAN (Density-based spatial clustering of applications with noise)
00:00
DBSCAN vs. OPTICS
00:00
Gaussian Mixture Models (GMMs)
00:00
Evaluation metrics for GMMs
00:00
Feature Engineering (Part-1)
00:00
Feature Engineering (Part-2)
00:00
Principal Component Analysis (PCA) – (Part-1)
00:00
Principal Component Analysis (PCA) – (Part-2)
00:00
SVD (Singular Value Decomposition)
00:00
tSNE (t-distributed Stochastic Neighbor Embedding)
00:00
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
00:00
Using Prompt Engineering to Craft Powerful Prompts with AI
00:00
Blogging Tips and Tricks using Prompt Engineering
00:00
Website Development Using Hostinger AI prompting
00:00
What we have learned so far?
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What we have learned so far?
00:00
Software Development
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Develop Software to do EDA in Python
00:00
Online Earning
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How to earn more using AI tools
00:00
Machine Learning one stop shop (Complete Project)
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Full Stack ML in Python
00:00
How to use Google Colab?
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Google Colab use, tips and tricks
00:00
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
00:00
What is Neural Network? and How we can construct it
00:00
Number of neurons in Each Layer
00:00
Computer Vision (Basics)
00:00
Deep Learning and Computer Vision
00:00
Activation Functions (Part-1)
00:00
Activation Functions (Part-2)
00:00
Activation Functions (Part-3)
00:00
How to choose an activation function?
00:00
Artificial Neural Network (Basic Network Structure)
00:00
ANN Advance Theory with Concepts
00:00
Regression and call back function
00:00
Binary and Multi-class classification
00:00
Training and Validation loss
00:00
Convulutional Neural Network (CNN)- (Part-1)
00:00
Convolutional Neural Network (CNN) Theory -(Part-2)
00:00
Convolutional Neural Network (CNN) Practice in Python-(Part-3)
00:00
Master Convolutional Neural Network (CNN) – (Part-4)
00:00
Recurrent Neural Network (RNN), LSTM and GRU in Python
00:00
Recurrent Neural Network (RNN), Time Series and NLP
00:00
15 Important Key terms for NLP and prompting
00:00
WebApp deployment
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Important Logins and websites
00:00
Web App development and Deployment using prompt engineering, chatGPT, github and streamlit cloud
00:00
All about git and gitHub
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git, gitTools and gitHub
00:00
Revision workshop
0/1
Recent Revision and HandsOn Workshop
00:00
Earn Online
0/1
How to earn Money online using AI and Data Science Skills?
00:00
FeedBack
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Your Feedback Matters
00:00
AI ka Chilla 2023- (Complete Artificial Intelligence Course in 40 Days)
    Join the conversation
    FAISAL SIKANDER 8 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 1 year ago
    ada Sir, Jazak ALLAH
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    Muhammad Rizwan Shoukat 1 year ago
    Ada Sir, Brilliant 👍👍👍
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    Muhammad Faizan Khan 1 year ago
    ada
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    Ilya Haider 1 year ago
    ada
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    Muhammad Haroon 1 year ago
    It is informative intro
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