It is clearly mentioned that this page is not designed / published by Sir Irfan Malik. I am a prospect student of Sir Irfan Malik and I published the syllabus and other relevant contents / links of the contents for guidance of fellow students.
Let's Join the Artificial Intelligence Drive initiated by Sir Irfan Malik to equip the less developed or even the under developed areas of Pakistan with the cutting edge technologies of the era i.e. Artificial Intelligence.
Special thanks and lots of prayers for the driving force behind the initiative Professor Dr Javed Iqbal Dr Azhar Aslam Sheraz Naseer Xeven Solutions
The number may vary from 20 to 30 The level of student is matriculation and Bachelors In Our Understanding the students of matriculation should be trained separately. As they are blank about the technology on the other hand the Bachelors students have background it will create an imbalance environment.
You can access official Resources for this life changeing course:
- Please click here to visit YouTube Channel
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- Please Follow at Linkedin
1st hour instructor will teach/demonstrate. The instructor will conduct a practice session on the lecture. At last the a small evaluation will be conducted.
To know the Program FAQs, please watch this video:
- Introduction to Chat Gpt and Interaction with it.
- Introduction to Dall-E and interaction with it.
- Introduction to Stable Diffusion and interaction with it
- Guideline about prompting.
- At Initial stage the students should interact with Open.ai tools like Chat GPT and DALL-E-2. This will greatly develop their interest and help them understand the products better. From this they will also learn the prompting which will help them later.
- Installing the IDE and Making Environments
- Basic Variables
- Data types
- String manipulation
- List
- Loops
- Tuples
- Dictionary
- JSON
- Functions
- Built in
- Custom
- Classes in python
- Declaration
- Initialization
- Code practise with Chat GPT
- Stage Evaluation
- Introduction the Machine Learning
- Supervised Learning
- Video demo
- Semi-supervised Learning
- Video demo
- Un-supervised Learning
- Video demo
- Re-inforcement learning
- Video demo
- Basics of ML Model
- Model
- Dataset
- Types of Data sets (Structured , Unstructured)
- Examples of Datasets
- Data preprocessing
- Data Cleaning (Missing Values and Outliers)
- Dimensionality Reduction
- Data Transformation
- Training process (Theory at this stage)
- Testing process (Theory at this stage)
- Evaluation Metric
- Loss functions
- Confusion matric
- Accuracy
- Precision
- Recall
- Stage Evaluation
- Introduction to API
- Basics of API
- Open.ai API
- Stable Diffusion API
- Fast API
- Stage Project 1: (NLP Project)
- Stage Project 2: (Image Generation Project)
- Stage Evaluation
- Understanding of Scikit-learn for Machine Learning Models
- Working with Structured Data (ETL Pipeline) Using Scikit-Learn
- Data Cleaning (Missing Values and Outliers)
- Dimensionality Reduction
- Data Transformation
- Concept of classification and regression
- Difference between them and where to use them
- Use case examples
- Creating Classification Models using Scikit-learn
- Evaluating Classification Models
- Creating Regression Models using Scikit-learn
- Evaluating Regression Models
- Creating Recommender System (Content Based and Collaborative Filtering based)
- Stage Project
- Basic concepts of Matplotlib
- Introduction to Visualisations
- Line plot
- Scatter plot
- Regression plot
- Bar charts
- Distribution plots
- Box plot
- Creating Visualisations using Seaborn
- Creating Visualisations using Plotly
- Stage Evaluation
- Introduction to Hugging Face
- Installation and Setup
- Text Classification using Pipelines
- Hands on practise
- Name Entity Recognition (NER) with Pipelines
- Hand on practise
- Sentiment Analysis With Pipelines
- Hands on practise
- Stage Evaluation