Course Curriculum
1. Introduction to Machine Learning & Deep Learning
- ML & DL Introduction & Overview
- Introduction to AI, ML, DL & Applications
- Introduction to Tools & Libraries
2. Data Analysis & Processing
- Introduction to Data & Types
- Signal, Speech Signals & Image Analysis & Processing
- Data Analytics
3. Machine Learning Algorithms
- Classification of ML Algorithms with Examples
- Supervised Machine Learning & Algorithms
- Unsupervised Machine Learning & Algorithms
- Reinforcement Machine Learning Algorithms
4. Neural Networks
- Introduction to Neurons & Networks
- Layers of Network
- Activation Layers
- Artificial Neural Network
- Convolutional Neural Network
- Layers in CNN
- Flatten Layer and Pooling Technique
- Activation Functions and Keras Metrics
- The Sequential Model
5. Advanced Deep Learning Techniques
- Classification Models & Object Recognition
- YOLO Model
- Introduction to Neural Network
- Keras Applications
- Prediction Class of Car Dataset using Keras API
6. Introduction to RNN
- Recurrent Neural Network Architecture
- Introduction to Long Short-Term Memory (LSTM)
- TensorBoard and Early Stopping in Keras
7. Natural Language Processing (NLP)
- Tokenization for NLP
- Stemming in NLP
- Lemmatization
- Stop Words in NLP
- Parts of Speech in NLP
- Disease Condition Detection from Drug Reviews
- Fake News Preprocessing
- Fake News Detection using LSTM
8. Real-Time Tasks
- Revolutionizing Car Recognition using Deep Learning Image Classification with Keras
- Drug Disease Prediction and Reviews using Natural Language Processing Technique
- Fake News Detection using LSTM-Based Deep Learning
- Brain Tumor Classification Using Image Classification Technique
- Diabetic Retinopathy Detection
- Kidney Tumor Detection and Classification
- Smart CCTV
- Stock Forecasting using Deep Learning Techniques