Generative AI/ML Online Training

generative ai/ml training - Details

AI / ML

Introduction to AI / ML

Introduction to AI, ML, DL, NLP, CV and its types

Setting up the development environment (Python, Jupyter Notebook, libraries: NumPy, Pandas, Scikit-learn, Tensorflow, PyTorch, OpenCV, NLTK, etc.)

Overview of the workflow and common techniques

Introduction to data science and its applications

Definition of data science and its role in various industries.

Explanation of the data science lifecycle and its key stages.

Overview of the different types of data: structured, unstructured, and semi-structured.

Discussion of the importance of data collection, data quality, and data preprocessing.

Exploratory data analysis (EDA)

I Introduction to Pandas, a Python library for data manipulation and analysis.

Overview of NumPy, a fundamental package for scientific computing with Python.

Explanation of key data structures in Pandas: Series and DataFrame

Hands-on exploration of data using Pandas to summarize, filter, and transform data.

Data cleaning techniques, handling missing values, and dealing with outliers.

Statistical analysis of data using NumPy functions.

Data Visualization using Matplotlib, Seaborn, and Plotly

Introduction to data visualization and its importance in data analysis.

Overview of Matplotlib, a popular plotting library in Python.

Exploring different types of plots: line plots, scatter plots, bar plots, histogram, etc.

Customizing plots with labels, titles, colors, and styles.

Introduction to Seaborn, a Python data visualization library based on Matplotlib.

Advanced plotting techniques with Seaborn: heatmaps, pair plots, and categorical plots.

Introduction to Plotly, an interactive plotting library for creating web-based visualizations.

Creating interactive and dynamic visualizations with Plotly

Hands-on: Instagram Reach Analysis

Data Engineering and Preprocessing

Introduction to Data Engineering: Data cleaning, transformation, and integration Data cleaning and Handling missing values: Imputation, deletion, and outlier treatment

Feature Engineering techniques: Creating new features, handling date and time variables, and encoding categorical variables

Data Scaling and Normalization: Standardization, min-max scaling, etc.

Dealing with categorical variables: One-hot encoding, label encoding, etc.

Web Scraping

Introduction to web scraping: Tools, libraries, and ethical considerations

Scraping data from websites using libraries like BeautifulSoup and requests: HTML parsing, locating elements, and extracting data

Handling different types of data on websites: Tables, forms, etc.

Storing scraped data in appropriate formats: CSV, JSON, or databases

Hands-on: Working on Scraping Data from Static / Dynamic Website

Supervised Learning - Regression

Introduction to Regression: Definition, types, and use cases

Linear Regression: Theory, cost function, gradient descent, and assumptions

Polynomial Regression: Adding polynomial terms, degree selection, and overfitting

Lasso and Ridge Regression: Regularization techniques for controlling model complexity

Evaluation metrics for regression models: Mean Squared Error (MSE), R-squared, and Mean Absolute Error (MAE)

Hands-On - Real Time Project

Supervised Learning - Classification

Introduction to Classification: Definition, types, and use cases

Logistic Regression: Theory, logistic function, binary and multiclass classification

Decision Trees: Construction, splitting criteria, pruning, and visualization

Random Forests: Ensemble learning, bagging, and feature importance

Evaluation metrics for classification models: Accuracy, Precision, Recall, F1-score, and ROC curves

Implementation of classification models using scikit-learn library

Hands-On - Heart Disease Detection & Food Order Prediction

SVM, KNN & Naive Bayes

Support Vector Machines (SVM): Study SVM theory, different kernel functions (linear, polynomial, radial basis function), and the margin concept. Implement SVM classification and regression, and evaluate the models.

K-Nearest Neighbors (KNN): Understand the KNN algorithm, distance metrics, and the concept of K in KNN. Implement KNN classification and regression, and evaluate the models.

Naive Bayes: Learn about the Naive Bayes algorithm, conditional probability, and Bayes' theorem. Implement Naive Bayes classification, and evaluate the model's performance.

Hands-On - Contact Tracing & Sarcasm Detection

Ensemble Methods and Boosting

AdaBoost: Boosting technique, weak learners, and iterative weight adjustment

Gradient Boosting (XGBoost): Gradient boosting algorithm, Regularization, and hyperparameter tuning

Evaluation and fine-tuning of ensemble models: Cross-validation, grid search, and model selection

Handling imbalanced datasets: Techniques for dealing with class imbalance, such as oversampling and undersampling

Hands-On - Medical Insurance Price Prediction

Deep Learning Basics

• What is Deep Learning?

• Difference between ML and DL.

• Why Deep Learning is needed?

• Real-world applications of DL.

• Neural Networks Basics

• Perceptron: The fundamental building block.

• Multi-Layer Perceptron (MLP): Hidden layers, activation functions.

• Activation Functions: ReLU, Sigmoid, Softmax, Tanh.

• Forward Propagation & Backpropagation.

• Loss Functions: MSE, Cross-Entropy.

• Optimization Algorithms: SGD, Adam, RMSprop.

• Types of Neural Networks

• Feedforward Neural Networks (FNNs).

• Convolutional Neural Networks (CNNs).

• Recurrent Neural Networks (RNNs).

• Transformers & Attention Mechanisms.

Model Evaluation and Hyperparameter Tuning

Hyperparameter tuning using GridSearchCV and RandomizedSearchCV

Model selection and comparison

Natural Language Processing (NLP)

Text Preprocessing: Learn about tokenization, stemming, lemmatization, stop word removal, and other techniques for text preprocessing.

Text Representation: Explore techniques such as Bag-of-Words (BoW), TF-IDF, and word embeddings (e.g., Word2Vec, GloVe) for representing text data.

Sentiment Analysis: Study sentiment analysis techniques, build a sentiment analysis model using supervised learning, and evaluate its performance.

Hands-On - Real Time Sentiment Analysis

Recommendation Systems

Introduction to Recommendation Systems: Understand the concept of recommendation systems, different types (collaborative filtering, content-based, hybrid), and evaluation metrics.

Collaborative Filtering: Explore collaborative filtering techniques, including user-based and item-based approaches, and implement a collaborative filtering model.

Content-Based Filtering: Study content-based filtering methods, such as TF-IDF and cosine similarity, and build a content-based recommendation system.

Deployment and Future Directions: Discuss the deployment of recommendation systems and explore advanced topics in NLP and recommendation systems.

Hands-On - News Recommendation System

Reinforcement Learning

- Introduction to Reinforcement Learning: Agent, environment, state, action, and reward

Markov Decision Processes (MDP): Markov property, transition probabilities, and value functions

Q-Learning algorithm: Exploration vs. exploitation, Q-table, and learning rate

Hands-On - Reinforcement Learning projects and exercises

Gen AI - Transformer Models

Introduction

Natural Language Processing

Transformers, what can they do?

How do Transformers work?

Encoder Models

Decoder Models

Sequence-to-Sequence Models

Bias and Limitations

Mastering NLP

Summary

Working with Audio

Introduction to audio data

Audio classification with a pipeline

Automatic speech recognition with a pipeline

Audio generation with a pipeline

Hands-on exercise

Hands-On - Speech-to-speech translation

Computer Vision

Imaging

Imaging in Real-life

What Is Computer Vision

Pre-processing for Computer Vision Tasks

Applications of Computer Vision

Feature Description

Real-world Applications of Feature Extraction in Computer Vision

Feature Matching

Hands-On - Real-Time Detection

Introduction to Prompt Engineering: Understanding the fundamentals and significance of prompt engineering in AI interactions.

• Prompting Techniques: Exploring various methods such as zero-shot, few-shot, and chain-of-thought prompting to enhance AI performance.

• Best Practices: Guidelines on clarity, specificity, context provision, and iterative refinement to craft effective prompts.

• Hands-On Exercises: Practical activities to apply learned techniques and improve prompt crafting skills.

• Ethical Considerations: Addressing biases in AI responses and promoting fairness and inclusivity in prompt design.

Devloping Webapp with Streamlit / Gradio / Flask and Development

Introduction to Flask / Streamlit / Gradio web framework

Creating a Flask / Streamlit / Gradio application for ML model deployment

Integrating data preprocessing and ML model

Designing a user-friendly web interface

Deployment using AWS / PythonAnywhere / Streamlit Cloud / Spaces

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generative ai/ml training - Details

AI / ML

Introduction to AI / ML

Introduction to data science and its applications

Exploratory data analysis (EDA)

Data Visualization using Matplotlib, Seaborn, and Plotly

Data Engineering and Preprocessing

Web Scraping

Supervised Learning - Regression

Supervised Learning - Classification

SVM, KNN & Naive Bayes

Ensemble Methods and Boosting

Deep Learning Basics

Model Evaluation and Hyperparameter Tuning

Natural Language Processing (NLP)

Recommendation Systems

Reinforcement Learning

Gen AI - Transformer Models

Working with Audio

Computer Vision

Introduction to Prompt Engineering: Understanding the fundamentals and significance of prompt engineering in AI interactions.

Devloping Webapp with Streamlit / Gradio / Flask and Development

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