- Welcome to the course!
- Introduction
- Real world applications of deep learning
- Download and install Anaconda
- Installation Video Guide
- Obtain the code for the course
- Course Folder Walkthrough
- Your first deep learning model
Online
₹ 455 3,499
Quick facts
particular | details | |
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
Deep Learning with Python and Keras online course is a short-term course developed by Data Weekends - Learn the essentials of Data Science in just one weekend, Jose Portilla - Head of Data Science, Pierian Data Inc., Instructor and presented by Udemy Inc., an ed-tech firm aimed at providing of online courses for professionals and beginners across 180 countries.
Deep Learning with Python and Keras certification course is intended to give you a thorough understanding of Deep Learning. The course is designed for Python beginners and intermediate programmers and data scientists who want to learn how to apply Deep Learning methods to a variety of situations.
Deep Learning with Python and Keras online training focuses on providing learners with firm ground, not just in theory, but also in coding. Learners will be taught to detect issues that can be solved using Deep Learning, develop a range of Neural Network models, and use cloud computing to boost the training and enhance their performance.
The highlights
- Certificate of completion
- Self-paced course
- English videos with multi-language subtitles
- 10 hours of pre-recorded video content
- Online course
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and TV
Program offerings
- Certificate of completion
- Self-paced course
- English videos with multi-language subtitles
- 10 hours of pre-recorded video content
- 6 articles
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and tv
Course and certificate fees
Fees information
certificate availability
certificate providing authority
What you will learn
After completing the Deep Learning with Python and Keras certification course, candidates will be able to demonstrate deep learning by applying it to develop the prediction models, identifying how the real world can be benefited from deep learning, and leveraging Python and Keras to develop deep learning models. Learners will be able to tackle supervised and unsupervised learning problems including images, text, sound, time series, and tabular data using deep learning, develop, train and use completely connected, convolutional, and recurrent neural networks. Learners will also be skilled to analyze the cost of training huge models, reduce training time and cost utilizing previously trained models.
Who it is for
The syllabus
Welcome to the course
Data
- Section 2 Intro
- Tabular data
- Data exploration with Pandas code along
- Visual Data Exploration
- Plotting with Matplotlib
- Unstructured Data
- Images and Sound in Jupyter
- Feature Engineering
- Exercise 1 Presentation
- Exercise 1 Solution
- Exercise 2 Presentation
- Exercise 2 Solution
- Exercise 3 Presentation
- Exercise 3 Solution
- Exercise 4 Presentation
- Exercise 4 Solution
- Exercise 5 Presentation
- Exercise 5 Solution
Machine Learning
- Section 3 Intro
- Machine Learning Problems
- Supervised Learning
- Linear Regression
- Cost Function
- Cost Function code along
- Finding the best model
- Linear Regression code
- Evaluating Performance
- Evaluating Performance code along
- Classification
- Classification code along
- Overfitting
- Cross-Validation
- Cross-Validation code along
- Confusion matrix
- Confusion Matrix code along
- Feature Preprocessing code along
- Exercise 1 Presentation
- Exercise 1 solution
- Exercise 2 Presentation
- Exercise 2 solution
Deep Learning
- Section 4 Intro
- Deep Learning successes
- Neural Networks
- Deeper Networks
- Neural Networks code along
- Multiple Outputs
- Multiclass classification code along
- Activation Functions
- Feedforward
- Exercise 1 Presentation
- Exercise 1 Solution
- Exercise 2 Presentation
- Exercise 2 Solution
- Exercise 3 Presentation
- Exercise 3 Solution
- Exercise 4 Presentation
- Exercise 4 Solution
Gradient Descent
- Section 5 Intro
- Derivatives and Gradient
- Backpropagation intuition
- Chain Rule
- Derivative Calculation
- Fully Connected Backpropagation
- Matrix Notation
- Numpy Arrays code along
- Learning Rate
- Learning Rate code along
- Gradient Descent
- Gradient Descent code along
- EWMA
- Optimizers
- Optimizers code along
- Initialization code along
- Inner Layers Visualization code along
- Exercise 1 Presentation
- Exercise 1 Solution
- Exercise 2 Presentation
- Exercise 2 Solution
- Exercise 3 Presentation
- Exercise 3 Solution
- Exercise 4 Presentation
- Exercise 4 Solution
- Tensorboard
Convolutional Neural Networks
- Section 6 Intro
- Features from Pixels
- MNIST Classification
- MNIST Classification code along
- Beyond Pixels
- Images as Tensors
- Tensor Math code along
- Convolution in 1 D
- Convolution in 1 D code along
- Convolution in 2 D
- Image Filters code along
- Convolutional Layers
- Convolutional Layers code along
- Pooling Layers
- Pooling Layers code along
- Convolutional Neural Networks
- Convolutional Neural Networks code along
- Weights in CNNs
- Beyond Images
- Exercise 1 Presentation
- Exercise 1 Solution
- Exercise 2 Presentation
- Exercise 2 Solution
Cloud GPUs
- Google Colaboratory GPU notebook setup
- Floyd GPU notebook setup
Recurrent Neural Networks
- Section 8 Intro
- Time Series
- Sequence problems
- Vanilla RNN
- LSTM and GRU
- Time Series Forecasting code along
- Time Series Forecasting with LSTM code along
- Rolling Windows
- Rolling Windows code along
- Exercise 1 Presentation
- Exercise 1 Solution
- Exercise 2 Presentation
- Exercise 2 Solution
Improving performance
- Section 9 Intro
- Learning curves
- Learning curves code along
- Batch Normalization
- Batch Normalization code along
- Dropout
- Dropout and Regularization code along
- Data Augmentation
- Continuous Learning
- Image Generator code along
- Hyperparameter search
- Embeddings
- Embeddings code along
- Movies Reviews Sentiment Analysis code along
- Exercise 1 Presentation
- Exercise 1 Solution
- Exercise 2 Presentation
- Exercise 2 Solution
- Exercise 3 Presentation
Instructors
Mr Jose Portilla
Head of Data Science
Udemy
Other Bachelors, M.S