Module #1 Introduction to Deep Learning Overview of deep learning, its applications, and importance in data analysis
Module #2 Python for Deep Learning Setting up Python for deep learning, essential libraries, and tools
Module #3 Mathematics for Deep Learning Review of linear algebra, calculus, and probability theory for deep learning
Module #4 Introduction to Neural Networks Basic concepts of neural networks, perceptrons, and multilayer perceptrons
Module #5 Keras for Deep Learning Introduction to Keras, its architecture, and building neural networks with Keras
Module #6 TensorFlow for Deep Learning Introduction to TensorFlow, its architecture, and building neural networks with TensorFlow
Module #7 Data Preprocessing for Deep Learning Importance of data preprocessing, techniques for handling missing values, and data normalization
Module #8 Convolutional Neural Networks (CNNs) Introduction to CNNs, architecture, and applications in image processing
Module #9 Recurrent Neural Networks (RNNs) Introduction to RNNs, architecture, and applications in sequence data analysis
Module #10 Long Short-Term Memory (LSTM) Networks Introduction to LSTMs, architecture, and applications in sequence data analysis
Module #11 Autoencoders Introduction to autoencoders, architecture, and applications in dimensionality reduction
Module #12 Unsupervised Learning with Deep Learning K-means clustering, hierarchical clustering, and dimensionality reduction with deep learning
Module #13 Supervised Learning with Deep Learning Regression, classification, and model evaluation with deep learning
Module #14 Deep Learning for Natural Language Processing (NLP) Introduction to NLP, tokenization, and word embeddings with deep learning
Module #15 Deep Learning for Computer Vision Image classification, object detection, and image segmentation with deep learning
Module #16 Transfer Learning and Fine-Tuning Introduction to transfer learning, pre-trained models, and fine-tuning
Module #17 Deep Learning with Big Data Scaling deep learning models with big data, distributed computing, and GPU acceleration
Module #18 Deep Learning Model Evaluation and Optimization Model evaluation metrics, hyperparameter tuning, and optimization techniques
Module #19 Handling Imbalanced Datasets with Deep Learning Techniques for handling class imbalance, oversampling, and undersampling
Module #20 Deep Learning for Time Series Analysis Introduction to time series analysis, forecasting, and anomaly detection with deep learning
Module #21 Deep Learning for Recommendation Systems Introduction to recommendation systems, collaborative filtering, and content-based filtering with deep learning
Module #22 Deep Learning for Generative Models Introduction to generative models, GANs, and VAEs
Module #23 Deep Learning with Python Libraries Using Python libraries like scikit-learn, TensorFlow, and Keras for deep learning
Module #24 Course Wrap-Up & Conclusion Planning next steps in Deep Learning for Data Analysis with Python career
