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Embedded Systems
Training
Understand the scope and prospects of Embedded Systems, learn how ML is being
used in various industries with our internship programs and develop the
necessary skills and Python programming to become a successful ML Engineer.
Learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, HR,
TensorFlow, and more!
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.
- 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
- 1. Neural Networks Fundamentals
- 2. CNNs, RNNs, and LSTMs
- 3. Deep Learning Frameworks (TensorFlow,
PyTorch)
- 4. Model Optimization and Tuning
- 1. Text Preprocessing Techniques
- 2. Sentiment Analysis
- 3. Word Embeddings & Transformers
- 4. Chatbots and Language Models
- 1. Image Processing Fundamentals
- 2. Object Detection & Recognition
- 3. Face Detection & Classification
- 4. Transfer Learning in CV
- 1. Git, GitHub, and Version Control
- 2. Streamlit & Flask for Model Deployment
- 3. Docker & Cloud Integration
- 4. MLOps Basics
- 1. Building a Portfolio
- 2. Mock Interviews & Feedback
- 3. Final Capstone Project Presentation
- 4. Connecting with Recruiters
Instructor: Jasika Peat, Data Scientist
4.7
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4,102+ learners
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