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 for deep learning operations.

Lavel

Beginner

Mode

Online

Fees

₹ 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

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

  • Welcome to the course
  • Introduction to Neural Networks and Course flow
  • Course Resources
  • This is a milestone!

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

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