Free Artificial Intelligence Learning Course

Stage 0: Orientation days (1)

• What it AI?
• What is future going to be like?
• Some Demos and interesting videos on it.
• Object detection
• Segmentation
• Classification
• Generative models
• Chatgpt
• Dall-e
• stable-diffusion
• Our Goal and Expectations from Students.
• Working plan.
• Work Ethics
• Practise sessions
• Evaluations
• Professional Grooming

Stage 1: Introduction with the Tools days (3 to 4)

• 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 the 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.

Stage 2: Basics of python days (20 to 30)

• 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

Stage 3: Basics of ML days (5 to 6)

• 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

Stage 4: Basics of API days (10 to 15)

• 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

Stage 5: Basics of ML frame work days (20 to 30)

• 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

Stage 6: Basics of Data Visualisations days (5 to 7)

• 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

Stage 7: Introduction to Hugging Face days (10 to 15)

• 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

You will learn

• Understanding of the fundamentals of artificial intelligence and its various applications.
• Familiarity with popular AI tools like ChatGPT, DALL-E, and Stable Diffusion.
• Proficiency in Python programming language and its data structures, control statements, functions, and classes.
• Knowledge of different types of machine learning, their applications, and the difference between supervised, unsupervised, semi-supervised, and reinforcement learning.
• Understanding of machine learning models, datasets, data preprocessing, training, testing, and evaluation metrics.
• Familiarity with different machine learning frameworks and their usage in creating structured data models.
• Knowledge of data visualization techniques using Matplotlib, Seaborn, and Plotly libraries.
• Familiarity with Hugging Face library and its usage in NLP tasks like text classification, NER, and sentiment analysis.