COLLABORATION WITH

IBM: AI Deep Learning with TensorFlow

Duration: 2 Weeks
Price: Rs 6499 (+18%GST)

Exam Objective Register Now
IBM: AI Deep Learning with TensorFlow
  1. 1. Self Paced ~ 50 Hrs
  2. 2. IBM Catalyst Certificate on successful completion
  3. 3. Unlimited lifetime access to the course

Earning Potential
  1. 1. $101,730 on an average annually.
  2. 2. Prove that you have the AI skills it takes to build a better world. Earning your IBM: AI Deep Learning with TensorFlow can supply the foundation you need to build your career.
What will you learn?
  1. 1. Basic and Advanced Python
  2. 2. Concept of Statistics
  3. 3. Conceptualization and working of Machine Learning
  4. 4. Artificial Intelligence and Neural Networks
  5. 6. Capstone Project
Skills Gained
  1. 1. Gain knowledge of common ML and AI workloads and how to implement them.
  2. 2. Python for ML, Deep Learning, Watson AI & Swift APIs, Apache
  3. 3. IBM: AI Deep Learning can be used to prepare for other role-based certifications like ML Scientist

Our Pricing Plan

Rs 6,499

(+18%GST)

  • Self Paced
  • Microsoft Certificate
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Program Lessons

AI - Prerequisite

Chapter 1 - Introduction to Artificial Intelligence
  • 1.1-Python Crash course Introduction
  • 1.2 Python Demo n install
  • 1.3 Python Intro and Installation
  • 1.4 Basic python and datatype
  • 1.5 Basic, Number, string
  • 1.6 Data types
Chapter-2 Introduction to Python
  • 2.1 Introduction to Python
  • 2.2 Introduction to Python Programming
  • 2.3 Basis of Python Programming
  • 2.4 Python Programming - 1
  • 2.5 Python Programming - 2
Chapter 3- Introduction to Matplotlib
  • 3.1 Introduction to Matplotlib
  • 3.2 Matplotlib Plots
  • 3.3 Matplotlib Styles - 1
  • 3.4 Matplotlib Styles - 2
  • 3.5 Matplotlib Plots Variants
Chapter 4- Introduction to Numpy
  • 4.1 Introduction to Numpy
  • 4.2 Numpy Types
  • 4.3 Numpy Arrays
  • 4.4 Numpy Array Attributes
  • 4.5 Numpy Math
Chapter 5- Introduction to Pandas
  • 5.1 Introduction to Pandas
  • 5.2 Pandas Reading Data
  • 5.3 Pandas Dataman - 1
  • 5.4 Pandas Dataman - 2
  • 5.5 Pandas Dataman - 3
Chapter 6 -Introduction to Machine Learning _ Unsupervised Learning
  • 6.1 Introduction to Machine Learning_UnSupervised Learning
Chapter 7 - Intro to Tensorflow
  • 7 Introduction to Tensorflow
  • 7.1 Introduction to Keras
  • 7.2 Clustering
  • 7.3 Introduction to Deep Learning
  • 7.4 Natural Language Processing
  • 7.5 Reinforcement Machine Learning

Deep Learning with TensorFlow - IBM

Module 1 – Introduction to TensorFlow
  • HelloWorld with TensorFlow
  • Linear Regression
  • Nonlinear Regression
  • Logistic Regression
  • Activation Functions
Module 2 – Convolutional Neural Networks (CNN)
  • CNN History
  • Understanding CNNs
  • CNN Application
Module 3 – Recurrent Neural Networks (RNN)
  • Intro to RNN Model
  • Long Short-Term memory (LSTM)
  • Recursive Neural Tensor Network Theory
  • Recurrent Neural Network Model
Module 4 - Unsupervised Learning
  • Applications of Unsupervised Learning
  • Restricted Boltzmann Machine
  • Collaborative Filtering with RBM
Module 5 - Autoencoders
  • Introduction to Autoencoders and Applications
  • Autoencoders
  • Deep Belief Network

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