Deep Learning for Data Analysis with Python
( 24 Modules )

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

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Deep Learning for Data Analysis with Python ( 24 Modules )

Deep Learning for Data Analysis with Python
( 24 Modules )