AI and ML Engineer Bootcamp

Subtopics

• Introduction to Python
• Control Structures and Functions
• Data Structures
• File Handling and Libraries

Learning Objective

• Understand basic Python syntax and data types 
• Learn control flow and function definitions 
• Use lists, dictionaries, sets, and tuples 
• Read and write files, use NumPy and Pandas

Expected Outcome

• Write simple Python programs
• Implement functions and control structures
• Manipulate data using Python data structures
• Handle data files and perform basic data manipulation

Use Cases/Projects

• Simple Python scripts
• Calculator program
• Data management script
• Data analysis using Pandas

Assessment Method

• Quiz
• Coding Exercise
• Lab
• Project

Subtopics

• Data Analysis with Pandas
• Data Visualization with Matplotlib & Seaborn
• Statistical Analysis

Learning Objective

• Analyze data using Pandas
• Create visualizations to represent data insights
• Understand statistical concepts and methods

Expected Outcome

• Perform data cleaning and analysis
• Generate plots and visual data stories
• Apply statistical techniques for data interpretation

Use Cases/Projects

• Sales data analysis
• Visual report on sales data
• Statistical analysis on survey data

Assessment Method

• Lab
• Quiz

Subtopics

• Introduction to Machine Learning
• Supervised Learning: Regression
• Supervised Learning: Classification
• Unsupervised Learning
• Model Evaluation and Hyperparameter Tuning

Learning Objective

• Grasp basic machine learning concepts
• Implement linear and logistic regression models
• Use decision trees, SVM, and KNN for classification tasks
• Explore clustering and dimensionality reduction techniques
• Optimize models using various evaluation metrics and techniques

Expected Outcome

• Differentiate between supervised and unsupervised learning
• Predict outcomes using regression techniques
• Classify data points accurately
• Group data using clustering algorithms
• Fine-tune models for better performance

Use Cases/Projects

• Overview report
• House price prediction Spam detection
• Customer segmentation
• Model evaluation and tuning

Assessment Method

• Quiz – 1
• Labs – 4

Subtopics

• Introduction to Neural Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Transfer Learning and Pre-trained Models

Learning Objective

• Learn the basics of neural networks and their applications
• Understand CNNs for image processing
• Learn RNNs for sequence modeling
• Utilize pre-trained models for various tasks

Expected Outcome

• Build simple neural networks Implement
• CNNs for image classification
• Model sequential data using RNNs
• Apply transfer learning to improve model performance

Use Cases/Projects

• Handwritten digit recognition
• Image classification project
• Text generation
• Fine-tuning pre-trained models

Assessment Method

• Project – 1
• Labs – 3

Subtopics

• Natural Language Processing
• Basics NLP Applications
• Introduction to AI Concepts

Learning Objective

• Understand text preprocessing and NLP basics
• Explore NLP applications like sentiment analysis and chatbots
• Overview of AI, reinforcement learning, and ethical considerations

Expected Outcome

• Process text data and extract meaningful information
• Build NLP models for real-world applications
• Apply AI techniques to various domains

Use Cases/Projects

• Sentiment analysis
• Chatbot development
• AI ethics case study

Assessment Method

• Lab
• Project
• Discussion

Subtopics

• Comprehensive Project

Learning Objective

• Integrate all learned concepts into a complete project

Expected Outcome

• Demonstrate practical knowledge through a capstone project

Use Cases/Projects

• Full data science or AI project

Assessment Method

• Project