Tensorflow Deep Learning - Data Science in Python

BY
Udemy

Develop a thorough understanding of TensorFlow for deep learning principles.

Mode

Online

Fees

₹ 449 2499

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course overview

For building Deep Learning models, Google introduced the TensorFlow framework. Artificial neural networks are a key component of the deep learning class of machine learning models. Tensorflow Deep Learning - Data Science in Python online certification is developed by Minerva Singh- Instructor & Data Scientist and is offered by Udemy for candidates who want to master the concepts involved with TensorFlow for data science operations using python programming.

Tensorflow Deep Learning - Data Science in Python online course contains 7 hours of video-based lessons supported by 3 articles and 45 downloadable resources to provide candidates a thorough understanding of statistical modeling, data visualization, machine learning, and fundamental deep learning using the Tensorflow framework in Python. By the end of the Tensorflow Deep Learning - Data Science in Python online training, candidates will be able to use packages such as Numpy, Pandas, and Matplotlib to collaborate with actual data in Python.

The highlights

  • Certificate of completion
  • Self-paced course
  • 7 hours of pre-recorded video content
  • 3 articles
  • 45 downloadable resources

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
₹ 449  ₹2,499
certificate availability

Yes

certificate providing authority

Udemy

What you will learn

Knowledge of deep learning Machine learning Knowledge of python Data science knowledge

After completing the Tensorflow Deep Learning - Data Science in Python certification course, candidates will acquire a solid understanding of the fundamentals of deep learning with TensorFlow for data science using python programming. In this deep learning course, candidates will explore the functionalities of Anaconda, and iPython, as well as will acquire the knowledge of the concepts involved with machine learning, statistical modeling, linear regression, neural network modeling, supervised learning, and unsupervised learning. In this deep learning certification, candidates will also learn about the strategies to work with neural networks including artificial neural networks and convolutional networks.

The syllabus

Introduction to TensorFlow: The key Concepts and Software Tools

  • Welcome to the World of TensorFlow
  • Introduction to the Course
  • Data and Scripts For the Course
  • What is Artificial Intelligence?
  • Python Data Science Environment
  • For Mac Users
  • Introduction to IPython
  • IPython in Browser
  • Install Tensorflow
  • Written Tensorflow Installation Instructions

Introduction to TensorFlow

  • A Brief Touchdown
  • A Brief Touchdown: Computational Graphs
  • Common Mathematical Operators in Tensorflow
  • A Tensorflow Session
  • Interactive Tensorflow Session
  • Constants and Variables in Tensorflow
  • Placeholders in Tensorflow
  • TensorBoard: Visualize Graphs in TensorFlow
  • Access TensorBoard Graphs

Other Python Packages and Their Interaction with TensorFlow

  • Miscellaneous Python Packages for Data Science
  • Introduction to Numpy
  • Create Numpy Arrays
  • Numpy Operations
  • Numpy for Statistical Operation
  • Introduction to Pandas
  • Read in Data from CSV
  • Read in Excel Data
  • Basic Data Cleaning
  • Convert to Tensor Objects

Statistical Modelling with TensorFlow

  • Correlation Analysis
  • Linear Regression-Theory
  • Linear Regression (From First Principles) With Tensorflow
  • Visualize the Results of OLS
  • Multiple Regression With Tensorflow-Part 1
  • Multiple Regression With Tensorflow-Machine Learning Approach
  • Estimate With Tensorflow Estimators
  • Multiple Regression With Tensorflow Estimators
  • More on Linear Regressor Estimator
  • GLM: Generalized Linear Model
  • Linear Classifier For Binary Classification
  • Accuracy Assessment For Binary Classification
  • Linear Classification with Binary Classification With Mixed Predictors

Introduction to Machine Learning

  • Introduction
  • What is Machine Learning?

Unsupervised Learning

  • What is Unsupervised Learning?
  • K-Means Clustering: Theory
  • Implement K-Means on Real Data

Supervised Learning

  • Softmax Classification
  • Random Forest (RF) for Binary Classification
  • Random Forest (RF) for Multiclass Classification
  • kNN- Classification

Artifical Neural Networks and Deep Learning with TensorFlow

  • Introduction to Artificial Neural Networks (ANN)
  • Multi-Layer Perceptron (MLP)
  • Deep Neural Network (DNN) Classifier
  • Deep Neural Network (DNN) Classifier With Mixed Predictors
  • Deep Neural Network (DNN) Regression
  • Wide and Deep Learning
  • Autoencoders Theory
  • Autoencoders for Credit Card Fraud Detection
  • Autoencoders for Multiple Classes

Convolution Neural Network (CNN) for Image Analysis

  • Introduction to CNN
  • Implement a CNN for Multi-Class Supervised Classification
  • Activation Functions
  • More on CNN
  • Pre-Requisite For Working With Imagery Data
  • CNN on Image Data
  • More on TFLearn
  • Autoencoders with CNN

Miscellaneous section

  • Use Colabs for Jupyter Data Science
  • Introduction To Github

Instructors

Ms Minerva Singh

Ms Minerva Singh
Data Scientist
Udemy

Other Masters, Ph.D, M.Phil.

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