Course Description

This course offers a comprehensive exploration of Machine Learning (ML) and Deep Learning (DL) concepts, providing an in-depth understanding of modern AI techniques and their applications. You will learn about the fundamental principles of ML and DL, data analysis, neural networks, and advanced deep learning techniques. This course is designed for those who want to build a robust foundation in ML and DL and apply these skills to solve real-world problems using state-of-the-art tools and libraries.

What You Will Learn

• An overview of Machine Learning (ML), Deep Learning (DL), and their applications in various fields.
• Key concepts in data analysis and processing, including signal, speech, and image analysis.
• Classification and implementation of different ML algorithms, including supervised, unsupervised, and reinforcement learning.
• Foundational and advanced concepts in neural networks, including Convolutional Neural Networks (CNNs) and activation functions.
• Advanced deep learning techniques such as YOLO models, RNNs, LSTMs, and their applications using Keras.
• Practical skills in Natural Language Processing (NLP), including text tokenization, stemming, lemmatization, and fake news detection.
• Hands-on experience with real-time projects that apply ML and DL techniques to solve complex problems.

Course Curriculum

• Introduction to Machine Learning & Deep Learning
  • ML & DL Introduction & Overview
  • Introduction to AI, ML, DL & Applications
  • Introduction to Tools & Libraries
• Data Analysis & Processing
  • Introduction to Data & Types
  • Signal, Speech Signals & Image Analysis & Processing
  • Data Analytics
• Machine Learning Algorithms
  • Classification of ML Algorithms with Examples
  • Supervised Machine Learning & Algorithms
  • Unsupervised Machine Learning & Algorithms
  • Reinforcement Machine Learning Algorithms
• 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
• Advanced Deep Learning Techniques
  • Classification Models & Object Recognition
  • YOLO Model
  • Introduction to Neural Network
  • Keras Applications
  • Prediction Class of Car Dataset using Keras API
  • Introduction to RNN
  • Recurrent Neural Network Architecture
  • Introduction to Long Short-Term Memory (LSTM)
  • TensorBoard and Early Stopping in Keras
• 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

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