Brochure
Enquire- Welcome to the course
- Introduction to Neural Networks and Course flow
- Course Resources
- This is a milestone!
Neural Networks in Python: Deep Learning for Beginners
BY
Udemy
Acquire a detailed understanding of the functionalities of programs like Python and Keras to work with neural networks ...Read more
Beginner
Online
₹ 3499
Quick Facts
| particular | details | |||
|---|---|---|---|---|
|
Medium of instructions
English
|
Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
|
Course overview
Neural networks, which help compensate for deep learning models, are typically built in a sequence of increasing the scale and abstraction. Neural networks are developed from a group of interconnected components that, like the synapses in the brain, can communicate with one another to function as interconnecting brain cells and understand and make decisions more like human beings. The Neural Networks in Python: Deep Learning for Beginners certification course is offered by Udemy and was designed by Start-Tech Academy, an institution of higher learning with over 160 locations worldwide.
Neural Networks in Python: Deep Learning for Beginners online course includes 9.5 hours of study material along with 4 articles, downloadable resources, practice exercises, assignments, and quizzes that revolve around topics like artificial neural networks, data processing, data manipulation, and statistical computation. Neural Networks in Python: Deep Learning for Beginners online classes also explain the functionalities of tools like Python, Keras, Tensorflow, Pandas, Numpy, and Seaborn to create neural networks for deep learning operations.
The highlights
- Certificate of completion
- Self-paced course
- 9.5 hours of pre-recorded video content
- 4 articles
- 1 downloadable resource
- 1 practice test
- Assignments
- Quizzes
Program offerings
- Online course
- Learning resources
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and tv
Course and certificate fees
Fees information
₹ 3,499
certificate availability
Yes
certificate providing authority
Udemy
Who it is for
What you will learn
Knowledge of python
Knowledge of deep learning
Knowledge of numpy
After completing the Neural Networks in Python: Deep Learning for Beginners online certification, candidates will gain a detailed understanding of the principles and functionalities associated with neural networks including artificial neural networks. Candidates will explore the functionalities of tools like Python, Keras, and TensorFlow for deep learning activities as well as will learn about various libraries including Numpy, Pandas, and Seaborn. Candidates will gain knowledge of the techniques used in data processing, hyperparameter tuning, linear regression, and statistical computation. Additionally, candidates will learn about techniques to work with pandas data frames and Jupiter notebook.
The syllabus
Introduction
Setting up Python and Jupyter Notebook
- Installing Python and Anaconda
- Opening Jupyter Notebook
- Introduction to Jupyter
- Arithmetic operators in Python: Python Basics
- Strings in Python: Python Basics
- Lists, Tuples and Directories: Python Basics
- Working with Numpy Library of Python
- Working with Pandas Library of Python
- Working with Seaborn Library of Python
Single Cells - Perceptron and Sigmoid Neuron
- Perceptron
- Activation Functions
- Python - Creating Perceptron model
Neural Networks - Stacking cells to create network
- Basic Terminologies
- Gradient Descent
- Back Propagation
Important concepts: Common Interview questions
- Some Important Concepts
- Quiz
Standard Model Parameters
- Hyperparameters
- Quiz
Practice Test
- Test your conceptual understanding
Tensorflow and Keras
- Keras and Tensorflow
- Installing Tensorflow and Keras
Python - Dataset for classification problem
- Dataset for classification
- Normalization and Test-Train split
- More about test-train split
Python - Building and training the Model
- Different ways to create ANN using Keras
- Building the Neural Network using Keras
- Compiling and Training the Neural Network model
- Evaluating performance and Predicting using Keras
Python - Solving a Regression problem using ANN
- Building Neural Network for Regression Problem
Complex ANN Architectures using Functional API
- Using Functional API for complex architectures
Saving and Restoring Models
- Saving - Restoring Models and Using Callbacks
Hyperparameter Tuning
- Hyperparameter Tuning
Add-on 1: Data Preprocessing
- Gathering Business Knowledge
- Data Exploration
- The Dataset and the Data Dictionary
- Add-on Resources
- Importing Data in Python
- Univariate analysis and EDD
- EDD in Python
- Outlier Treatment
- Outlier Treatment in Python
- Missing Value Imputation
- Missing Value Imputation in Python
- Seasonality in Data
- Bi-variate analysis and Variable transformation
- Variable transformation and deletion in Python
- Non-usable variables
- Dummy variable creation: Handling qualitative data
- Dummy variable creation in Python
- Correlation Analysis
- Correlation Analysis in Python
Add-on 2: Classic ML models - Linear Regression
- The Problem Statement
- Basic Equations and Ordinary Least Squares (OLS) method
- Assessing accuracy of predicted coefficients
- Assessing Model Accuracy: RSE and R squared
- Simple Linear Regression in Python
- Multiple Linear Regression
- The F - statistic
- Interpreting results of Categorical variables
- Multiple Linear Regression in Python
- Test-train split
- Bias Variance trade-off
- Test train split in Python
Practice Assignment
- Neural Networks Classification Assignment
Bonus Section
- The final milestone!
- Congratulations & About your certificate
Articles
Popular Articles
prev
next
Latest Articles
prev
next
Courses of your Interest
C++ Foundation
PW Skills
Advanced CFD Meshing using ANSA
Skill Lync
Data Science Foundations to Core Bootcamp
Springboard
User Experience Design And Research
UM–Ann Arbor via Futurelearn
Fundamentals of Agile Project Management
UCI Irvine via Futurelearn
Artificial intelligence Design and Engineering wit...
CloudSwyft Global Systems, Inc via Futurelearn
prev
next
More Courses by Udemy
prev
next
