Artificial Intelligence (AI) Free Basic Course In this course, you will learn about the fundamentals of Artificial Intelligence and how it is transforming the world.
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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
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.
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
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.