Embedded Systems

Learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, HR, TensorFlow, and more!

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Course Overview

This Embedded Systems course is designed to provide a comprehensive introduction to ML concepts, algorithms, and applications. You'll work on real-world projects, learn Python programming, and build models using popular libraries like Scikit-learn, Pandas, and TensorFlow.

Module 1: Introduction

• 1. Introduction to Embedded Systems
• 2. Python for ML – Basics to Advanced
• 3. Data Preprocessing and Feature Engineering
• 4. Supervised & Unsupervised Learning Algorithms
• 5. Real-world Projects and Capstone
• 6. Resume & Interview Preparation

Module 2: Deep Learning

• 1. Neural Networks Fundamentals
• 2. CNNs, RNNs, and LSTMs
• 3. Deep Learning Frameworks (TensorFlow, PyTorch)
• 4. Model Optimization and Tuning

Module 3: NLP & Text Analytics

• 1. Text Preprocessing Techniques
• 2. Sentiment Analysis
• 3. Word Embeddings & Transformers
• 4. Chatbots and Language Models

Module 4: Computer Vision

• 1. Image Processing Fundamentals
• 2. Object Detection & Recognition
• 3. Face Detection & Classification
• 4. Transfer Learning in CV

Module 5: Tools & Deployment

• 1. Git, GitHub, and Version Control
• 2. Streamlit & Flask for Model Deployment
• 3. Docker & Cloud Integration
• 4. MLOps Basics

Module 6: Career Prep & Final Project

• 1. Building a Portfolio
• 2. Mock Interviews & Feedback
• 3. Final Capstone Project Presentation
• 4. Connecting with Recruiters

Instructor: Jasika Peat, Data Scientist

4.7 ⭐⭐⭐⭐⭐ 4,102+ learners