Artificial Intelligence Bootcamp

Installation

• Introduction to the Python programming language and its applications
• Setting up the Python environment: installation of Python and necessary libraries

Python Basics I

• Introduction to Python: history, features, and advantages
• Expressions and operators: arithmetic, assignment, comparison, and logical
• Understanding type() function and type inference
• Introduction to data structures: lists, tuples, and dictionaries

 Python Basics II

• Recap of Python basics
• Working with arithmetic operators: addition, subtraction, multiplication, division, modulus, and exponentiation
• Using comparison operators: equal to, not equal to, greater than, less than, etc.
• Logical operators: and, or, and not
• Exploring advanced data types: sets and strings manipulation.

Expressions, Conditional Statements & For Loop

• Evaluating expressions: operator precedence and associativity
• Introduction to conditional statements: if, elif, and else
• Executing code based on conditionals.
• Understanding the flow of control in conditional statements
• Iteration using the for loop: range(), iteration over lists, and strings.

While loop, Break and Continue Statements, and Nested Loops

• Working with while loop: syntax, conditions, and examples
• Combining loops and conditionals
• Using the break statement to exit loops prematurely.
• Utilizing the continue statement to skip iterations.
• Implementing nested loops for complex iterations

Functions

• Introduction to functions: purpose, advantages, and best practices
• Defining and calling user-defined functions
• Parameters and arguments: positional, keyword, and default values
• Return statement and function output.
• Variable scope and lifetime
• Function documentation and code readability

Exception Handling and File Handling

• Understanding exceptions: errors, exceptions, and exception hierarchy
• Handling exceptions using try-except blocks: handling specific exceptions, multiple exceptions, and else and finally clauses.
• Raising exceptions and creating custom exception classes
• File handling in Python: opening, reading, writing, and closing files.
• Working with different file modes and file objects

Python Modules: NumPy,Pandas and Matplotlib

• Introduction to the NumPy module: features and applications
• Working with multidimensional arrays: creation, indexing, slicing, and reshaping
• Performing element-wise operations: arithmetic, logical, and statistical
• Overview of the Matplotlib module: data visualization and plotting
• Customizing plots: line properties, markers, colors, labels, and legends

Advanced Topics

• Introduction to Kaggle platform: features and benefits
• Leveraging Kaggle for real-life datasets: data exploration, analysis, and visualization

Introduction and Missing Value Analysis

• Introduction to Exploratory Data Analysis (EDA)
• Importance of EDA in data analysis
• Steps involved in EDA
• Handling missing values: identification, analysis, and treatment strategies • Imputation techniques for missing values

Data Consistency, Binning, and Outlier Analysis

• Data consistency checks using fuzzy logic
• Binning and discretization techniques for continuous variables
• Outlier detection and analysis methods
• Handling outliers: techniques for treatment or removal

 Feature Selection and Data Wrangling

• Importance of feature selection in EDA
• Feature selection techniques: filter methods, wrapper methods, and embedded methods
• Data wrangling: cleaning and transforming data for analysis
• Handling categorical variables: encoding techniques

  Inference, Hypothesis Testing, and Visualization

• Inference and hypothesis testing in EDA
• Common statistical tests: t-test, chi-square test, ANOVA, etc.
• Visualization techniques for EDA: histograms, box plots, scatter plots, etc. 
• Hands-on  practical session for complete EDA using a dataset

Machine Learning Performance Metrics and Naive Bayes

• Evaluation metrics for classification problems: accuracy, precision, recall, F1 score, etc.
• Introduction to Naive Bayes algorithm and its applications
• Implementing Naive Bayes for classification tasks

Logistic Regression, SVM, Decision Trees, and Random Forests

• Logistic Regression: theory, interpretation, and applications
• Support Vector Machines (SVM): concepts, kernels, and use cases
• Decision Trees: construction, pruning, and interpretability
• Random Forests: ensemble learning and feature importance
• Bagging and Boosting: techniques for improving model performance

Hyperparameter Tuning, PCA, and SVD

• Hyperparameter tuning techniques: grid search, random search, and Bayesian optimization
• Principal Component Analysis (PCA): dimensionality reduction and feature extraction
• Singular Value Decomposition (SVD): applications in matrix factorization and data compression

Clustering Introduction, Partitioning Algorithms, and Cluster Evaluation

• Introduction to clustering: unsupervised learning technique
• Partitioning algorithms: K-means, K-medoids
• Hierarchical clustering: agglomerative and divisive approaches
• Density-based clustering: DBSCAN, OPTICS
• Cluster evaluation metrics: silhouette coefficient, Davies-Bouldin index

Regression and Evaluation of Regression Methods

• Introduction to regression analysis
• Linear regression: assumptions, interpretation, and model evaluation • Evaluation metrics for regression: mean squared error, R-squared, etc.
• Other regression methods: polynomial regression, ridge regression, lasso regression

Introduction to Time Series and Time Series Forecasting 

• Concepts and characteristics of time series data
• Time series components: trend, seasonality, and noise
• Popular time series forecasting models: ARIMA, SARIMA, exponential smoothing • Implementing time series forecasting models

Models and Hyperparameter Tuning

• Evaluation metrics for time series forecasting: mean absolute error, mean absolute percentage error, etc.
• Cross-validation techniques for time series data
• Hyperparameter tuning for time series models

• Introduction to NLP and Text Normalization
• Overview of Natural Language Processing (NLP)
• Techniques for text normalization: lowercasing, punctuation removal, etc.
  
  
• Text Representation and Tokenization

• • Introduction to vectors in NLP: Bag of Words and Count Vectorizer
  • Basics of tokenization and stopword removal
    
    

• Stemming, Lemmatization, and N-gram Language Models

• • Understanding and applying stemming and lemmatization
  • Introduction to N-gram language models

•  Markov Models and Language Model Evaluation

• • Basics of Markov models in NLP
  • Techniques for evaluating language models: probability smoothing and performance metrics

• Text Classification Fundamentals

• • Overview of Text Classification
  • Introduction to Naive Bayes and Sentiment Classification

• Advanced Classifiers and Vector Semantics

• • Generative vs. discriminative Classifiers
  • Understanding vector semantics and embeddings: TF-IDF theory and vector similarity

• Neural Word Embeddings and Sequence Models

• • • Introduction to neural word embeddings: Word2Vec and GloVe
    • Exploring sequence of words in NLP tasks

• Transformers and Large Language Models

• • Overview of transformers and their impact on NLP
  • Introduction to large language models (LLMs) and their applications

Computer Vision

• Cascade and HOG classifiers to detect faces
• Face detection using OpenCV and Dlib library
• Detect other objects using OpenCV, such as cars, clocks, eyes, and full body of people
• KCF and CSRT algorithms to perform object tracking
• convolutional neural networks and implement them using Python and TensorFlow
• Detect objects in images in videos using YOLO, one of the most powerful algorithms today
• Recognize gestures and actions in videos using OpenCV
• Create hallucinogenic images with Deep Dream
• Create images that don't exist in the real world with GANs (Generative Adversarial Networks)

Machine Learning Operations(MLOps)

•  Introduction to MLOps and AI/NLP Fundamentals

• Overview of MLOps and its importance in the AI lifecycle
• Current trends in AI
• Setting up the development environment

•  Deep Dive into Machine Learning Models for NLP

• Understanding NLP models (llama2, GPT, Mistral, etc.)
• Introduction to Hugging Face Transformers and Datasets
• Hands-on: Building a simple NLP model with Hugging Face

•  Introduction to FastAPI for ML Model Deployment
  • Basics of API development with FastAPI
  • Deploying a simple ML model with FastAPI
  • Hands-on: Creating your first ML API with FastAPI

•  Advanced FastAPI Features for Production-Ready APIs
  • Authentication and authorization in FastAPI
  • Hands-on: Enhancing your ML API with advanced features

•  Introduction to Docker for AI Applications
  • Basics of Docker and containerization
  • Building Docker images for AI/ML applications
  • Hands-on: Containerizing your FastAPI application

• Leveraging Lang Chain and LangSmith for Enhanced NLP Applications

• • • Introduction to Lang Chain and its Ecosystem
    • Overview of LangSmith for debugging, testing, evaluating, and monitoring LLM applications
    • Hands-on: Integrating Lang Chain with your NLP models and using LangSmith for enhanced capabilities

• Advanced Model Deployment with Hugging Face and Lang Chain

• • Integrating Hugging Face models for advanced NLP capabilities
  • Exploring Lang Chain for building complex NLP applications
  • Hands-on: Deploying a Hugging Face model via FastAPI with LangSmith integration

• Deploying ML Models on Google Cloud

• Overview of Google Cloud Platform (GCP) for ML
• Introduction to Google Cloud Run
• Hands-on: Deploying your Dockerized FastAPI application on GCP with LangSmith monitoring

• Email Writing

  • Basics of Professional Email Communication: Structure, tone, and etiquette.
  • Writing Effective Subject Lines: Techniques to ensure your emails are opened.
  • Emails for Networking: Approaching professionals and mentors in data science/AI.
  • Follow-up Emails: Strategies for following up without being intrusive.

• Report Writing + Presentations

  • Structure of a Data Science Report: Elements including abstract, methodology, results, and conclusion.
  • Visualizing Data: Incorporating charts, graphs, and other visual tools to enhance comprehension.
  • Creating Engaging Presentations: Tips for PowerPoint, storytelling, and engaging your audience.
  • Presentation Skills: Delivering your message confidently, handling Q&A sessions.

• LinkedIn Optimization

  • Building a Professional Profile: Key components of a LinkedIn profile for data science/AI professionals.
  • Networking Strategies: Connecting with industry professionals and joining relevant groups.
  • Content Sharing and Creation: Establishing thought leadership by sharing insights, articles, and engaging with community content.

• Resume/CV Writing

  • Tailoring Your Resume for Data Science/AI: Highlighting relevant skills, projects, and experiences.
  • Action Verbs and Quantifiable Achievements: Demonstrating impact in previous roles or projects.
  • Design and Layout: Making your resume/CV visually appealing and easy to read.

• Cover Letter

  • Structure of a Cover Letter: Introduction, body, and closing.
  • Customizing Your Message: Researching the company and role to personalize content.
  • Highlighting Fit and Value: Articulating how your skills and experiences align with the job requirements.

• Freelancing

  • Getting Started with Freelancing: Platforms for data science/AI freelancers, setting up a profile.
  • Finding Projects and Clients: Strategies to secure freelance work and build a portfolio.
  • Pricing Your Services: Understanding market rates and value-based pricing.
  • Client Management: Communicating effectively and managing expectations.

• Kaggle for Data Science

  • Introduction to Kaggle: Overview of the platform, competitions, datasets, and notebooks.
  • Participating in Competitions: Tips for success, collaboration, and learning from the community.
  • Building a Portfolio: Using Kaggle to showcase your skills and projects to potential employers.

• GitHub

  • Why GitHub for Data Scientists: Importance of version control and code sharing.
  • Creating and Managing Repositories: Best practices for organizing and documenting projects.
  • Collaborating on Projects: Contributing to open-source projects and collaborating with others.
  • GitHub as a Portfolio: Presenting your work and contributions to potential employers.

• How to Crack Data Science Interviews

  • Understanding the Interview Process: Types of interviews (technical, behavioral, case studies).
  • Preparing for Technical Interviews: Common questions, coding challenges, and statistical questions.
  • Behavioral Interview Preparation: Crafting your story, STAR method for responses.
  • Mock Interviews: Practicing with peers or mentors to gain confidence.

• Global Market Understanding

  • Data Science/AI Trends: Understanding global trends and emerging technologies.
  • Cultural Competence: Working in multicultural teams and serving diverse user bases.
  • Regulatory Environment: Overview of data privacy laws and ethical considerations in different regions.

• AI Product Development

  • From Idea to Product: Ideation, validation, and development processes.
  • User-Centric Design: Incorporating user feedback and UX/UI principles.
  • Product Management for AI: Unique challenges in managing AI projects, iteration, and deployment.
  • Metrics and Performance: Evaluating the success and impact of AI products.

• Storytelling Using Data

  • Principles of Data Storytelling: Crafting narratives that resonate with your audience.
  • Visual Narrative Techniques: Using data visualizations effectively in your story.
  • Engaging Presentations: Combining data, visuals, and narrative for impactful presentations.

• Intro to Data Commons

  • Understanding Data Commons: Concept, importance, and examples.
  • Accessing and Contributing to Data Commons: Guidelines and best practices.
  • Leveraging Data Commons: How data scientists can use these resources for research and development.