Brochure
Enquire- Introduction and Outline
- Where to get the code
- How to Succeed in this Course
Data Science: Deep Learning and Neural Networks in Python
BY
Udemy
Learn about data science and how to use deep learning and neural networks in Python to improve your skills and ...Read more
Online
₹ 4299
Quick Facts
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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
Data Science: Deep Learning and Neural Networks in Python online certification are developed by Lazy Programmer Inc., artificial intelligence and machine learning engineer, and offered by Udemy Inc., a US-based online learning platform to help individuals boost their careers. The course is designed for individuals who are interested in mastering their skills in deep learning, machine learning, and data science.
Data Science: Deep Learning and Neural Networks in Python online course will teach the learners to use deep learning techniques to create their artificial neural network, fundamental building blocks of neural networks, and use Python and Numpy to create full-fledged non-linear neural networks right away.
Data Science: Deep Learning and Neural Networks in Python online training are focused on the professionals with a background in programming as the course come with some prerequisites such as knowledge of calculus (taking derivatives), matrix arithmetic, probability, python coding, NumPy coding, linear regression, and logistic regression. candidates can also avail discounts at the time of checkout, if available.
The highlights
- Certificate of completion
- Self-paced course
- Online course
- English videos with multi-language subtitles
- 12 hours of pre-recorded video content
- 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
- Pre-recorded video content
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and tv
- 12 hours of videos
Course and certificate fees
Fees information
₹ 4,299
certificate availability
Yes
certificate providing authority
Udemy
Who it is for
What you will learn
Machine learning
Data science knowledge
Knowledge of deep learning
After completing the Data Science: Deep Learning and Neural Networks in Python certification course, learners will develop an understanding of the process of deep learning, neural network and its building blocks, coding a neural network in Python, NumPy, and Google TensorFlow. Learners will get to know about types of neural networks and their use in certain problems, and get an understanding of the topics such as activation, backpropagation, and feedforward.
The syllabus
Welcome
Review
- Review Section Introduction
- What does machine learning do?
- Neuron Predictions
- Neuron Training
- Deep Learning Readiness Test
- Review Section Summary
Preliminaries: From Neurons to Neural Networks
- Neural Networks with No Math
- Introduction to the E-Commerce Course Project
Classifying more than 2 things at a time
- Prediction: Section Introduction and Outline
- From Logistic Regression to Neural Networks
- Interpreting the Weights of a Neural Network
- Softmax
- Sigmoid vs. Softmax
- Feedforward in Slow-Mo (part 1)
- Feedforward in Slow-Mo (part 2)
- Where to get the code for this course
- Softmax in Code
- Building an entire feedforward neural network in Python
- E-Commerce Course Project: Pre-Processing the Data
- E-Commerce Course Project: Making Predictions
- Prediction Quizzes
- Prediction: Section Summary
- Suggestion Box
Training a neural network
- Training: Section Introduction and Outline
- What do all these symbols and letters mean?
- What does it mean to "train" a neural network?
- How to Brace Yourself to Learn Backpropagation
- Categorical Cross-Entropy Loss Function
- Training Logistic Regression with Softmax (part 1)
- Training Logistic Regression with Softmax (part 2)
- Backpropagation (part 1)
- Backpropagation (part 2)
- Backpropagation in code
- Backpropagation (part 3)
- The WRONG Way to Learn Backpropagation
- E-Commerce Course Project: Training Logistic Regression with Softmax
- E-Commerce Course Project: Training a Neural Network
- Training Quiz
- Training: Section Summary
Practical Machine Learning
- Practical Issues: Section Introduction and Outline
- Donut and XOR Review
- Donut and XOR Revisited
- Neural Networks for Regression
- Common nonlinearities and their derivatives
- Practical Considerations for Choosing Activation Functions
- Hyperparameters and Cross-Validation
- Manually Choosing Learning Rate and Regularization Penalty
- Why Divide by Square Root of D?
- Practical Issues: Section Summary
TensorFlow, exercises, practice, and what to learn next
- TensorFlow plug-and-play example
- Visualizing what a neural network has learned using TensorFlow Playground
- Where to go from here
- You know more than you think you know
- How to get good at deep learning + exercises
- Deep neural networks in just 3 lines of code with Sci-Kit Learn
Project: Facial Expression Recognition
- Facial Expression Recognition Project Introduction
- Facial Expression Recognition Problem Description
- The class imbalance problem
- Utilities walkthrough
- Facial Expression Recognition in Code (Binary / Sigmoid)
- Facial Expression Recognition in Code (Logistic Regression Softmax)
- Facial Expression Recognition in Code (ANN Softmax)
- Facial Expression Recognition Project Summary
Backpropagation Supplementary Lectures
- Backpropagation Supplementary Lectures Introduction
- Why Learn the Ins and Outs of Backpropagation?
- Gradient Descent Tutorial
- Help with Softmax Derivative
- Backpropagation with Softmax Troubleshooting
Higher-Level Discussion
- What's the difference between "neural networks" and "deep learning"?
- Who should take this course in 2020 and beyond?
- Who should learn backpropagation in 2020 and beyond?
- Where does this course fit into your deep learning studies?
Setting Up Your Environment (FAQ by Student Request)
- Anaconda Environment Setup
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Extra Help With Python Coding for Beginners (FAQ by Student Request)
- How to Uncompress a .tar.gz file
- How to Code by Yourself (part 1)
- How to Code by Yourself (part 2)
- Proof that using Jupyter Notebook is the same as not using it
- Python 2 vs Python 3
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
- How to Succeed in this Course (Long Version)
- Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
- Where does this course fit into your deep learning studies? (Old Version)
- Machine Learning and AI Prerequisite Roadmap (pt 1)
- Machine Learning and AI Prerequisite Roadmap (pt 2)
Appendix / FAQ Finale
- What is the Appendix?
- BONUS
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