Ensure career success with this Artificial Intelligence course, in collaboration with IBM. Featuring exclusive IBM hackathons, masterclasses, & Ask me anything sessions, this AI certification training helps you master key concepts including Data Science with Python, Machine Learning, Deep Learning, & NLP. Moreover, get job-ready AI Deep Learning Training with live sessions, practical labs, and projects. Enroll & Get Certified now!

  • ✔ Course Duration : 32 hrs
  • ✔ Training Options : Live Online / Self-Paced / Classroom
  • ✔ Certification Pass : Guaranteed

32 hrs

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20+ hrs



AI Deep learning Training | AI Deep learning Master Course | AI Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition and artificial intelligence. In this course we will start with deep learning introduction and you’ll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting edge library for deep learning. Mildaintrainings provides The Best Deep Learning Course by Industry Experts.

What you will Learn

  • Gain a Strong Understanding of TensorFlow - Google’s Cutting-Edge Deep Learning Framework
  • Set Yourself Apart with Hands-on Deep and Machine Learning Experience
  • Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules
  • Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding
  • Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow
  • Grasp the Mathematics Behind Deep Learning Algorithms
  • Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization


  • Some basic Python programming skills
  • You’ll need to install Anaconda. We will show you how to do it in one of the first lectures of the course.
  • All software and data used in the course are free.


Introduction to deep learning
  • What is a neural network?
  • Supervised Learning with Neural Networks
  • Why is Deep Learning taking off?
  • Introduction to deep learning
Neural Networks Basics
  • Binary Classification
  • Logistic Regression
  • Logistic Regression Cost Function
  • Gradient Descent
  • Derivatives
  • Computation graph
  • Derivatives with a Computation Graph
  • Logistic Regression Gradient Descent

  • Vectorizing Logistic Regression
  • Vectorizing Logistic Regression's Gradient Output
  • Broadcasting in Python
  • A note on python/numpy vectors
  • Quick tour of Jupyter/iPython Notebooks
  • Explanation of logistic regression cost function (optional)

Shallow neural networks
  • Neural Networks Overview
  • Deep Learning Honor Code
  • Logistic Regression with a Neural Network mindset
  • Neural Network Representation
  • Computing a Neural Network's Output
  • Vectorizing across multiple examples
  • Explanation for Vectorized Implementation
  • Activation functions
  • Why do you need non-linear activation functions?
  • Derivatives of activation functions
  • Gradient descent for Neural Networks
  • Backpropagation intuition (optional)
  • Random Initialization
Deep Neural Networks
  • Deep L-layer neural network
  • Forward Propagation in a Deep Network
  • Getting your matrix dimensions right
  • Why deep representations?
  • Building blocks of deep neural networks
  • Forward and Backward Propagation
  • Parameters vs. Hyperparameters
  • What does this have to do with the brain?
  • Deep Neural Network – Application

Key concepts on Deep Neural Networks
  • Building your Deep Neural Network: Step by Step
  • Deep Neural Network - Application
Foundations of Convolutional Neural Networks
  • Computer Vision
  • Edge Detection
  • Padding
  • Strided Convolutions
  • Convolutions Over Volume
  • One Layer of a Convolutional Network
  • Simple Convolutional Network Example
  • Pooling Layers
  • Why Convolutions?

Deep convolutional models: case studies

Object detection

  • Object Localization
  • Landmark Detection
  • Object Detection
  • Convolutional Implementation of Sliding Windows
  • Bounding Box Predictions
  • Intersection Over Union
  • Non-max Suppression
  • Anchor Boxes
  • YOLO Algorithm

Special applications: Face recognition & Neural style transfer
  • What is face recognition?
  • Siamese Network
  • Triplet Loss
  • Face Verification and Binary Classification
  • What is neural style transfer?
  • What are deep ConvNets learning?
  • Cost Function
  • Content Cost Function
  • Style Cost Function
  • 1D and 3D Generalizations
  • Face Recognition for the Happy House
Recurrent Neural Networks
  • Why sequence models
  • Notation
  • Recurrent Neural Network Model
  • Back propagation through time
  • Different types of RNNs
  • Language model and sequence generation
  • Sampling novel sequences
  • Vanishing gradients with RNNs
  • Gated Recurrent Unit (GRU)
  • Long Short Term Memory (LSTM)
  • Bidirectional RNN
  • Deep RNNs
  • Building a recurrent neural network - step by step
  • Dinosaur Island - Character-Level Language Modeling
  • Other-Jazz improvisation with LSTM

Natural Language Processing & Word Embeddings
  • Word Representation
  • Using word embeddings
  • Properties of word embeddings
  • Embedding matrix
  • Learning word embeddings
  • Word2Vec
  • Negative Sampling
  • GloVe word vectors
  • Sentiment Classification
  • Debiasing word embeddings
  • Operations on word vectors – Debiasing

Sequence models & Attention mechanism
  • Basic Models
  • Picking the most likely sentence
  • Beam Search
  • Refinements to Beam Search
  • Error analysis in beam search
  • Bleu Score (optional)
  • Attention Model Intuition
  • Attention Model
  • Speech recognition
  • Trigger Word Detection
  • Neural Machine Translation with Attention
  • Trigger word detection


You can enroll for this classroom training online. Payments can be made using any of the following options and receipt of the same will be issued to the candidate automatically via email.
1. Online ,By deposit the mildain bank account
2. Pay by cash team training center location

Highly qualified and certified instructors with 20+ years of experience deliver more than 200+ classroom training.

Contact us using the form on the right of any page on the mildaintrainings website, or select the Live Chat link. Our customer service representatives will be able to give you more details.

You will never miss a lecture at Mildaintrainigs! You can choose either of the two options: View the recorded session of the class available in your LMS. You can attend the missed session, in any other live batch.

We have a limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.

Yes, you can cancel your enrollment if necessary prior to 3rd session i.e first two sessions will be for your evaluation. We will refund the full amount without deducting any fee for more details check our Refund Policy

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

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Training Features

Experiential Workshops

Top-rated instructors imparting in-depth training, hands-on exercises with high energy workshop

Certificate Exam Application Assistance

The training program includes several lab assignments, developed as per real industry scenarios.

Certificate Exam Success Formula

Training begins taking a fresh approach from basic, unique modules, flexible, and enjoyable.

Certificate Journey Support

Basic to intermediate and eventually advanced practicing full hands-on lab exercises till you master.

Free Refresh Course

Refresh training for experts for mastering and enhancing the skills on the subjects with fresh course modules.

Exclusive Post-Training Sessions

Includes evaluation, feedback, and tips to handle critical issues in live setup after you are placed in a job.

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