Certified Deep Learning Expert Course

Deep Learning happens to be a subset of Machine Learning and helps to shadow the limitations evident in the machine learning functions. Where the data is large in number, deep learning is the ideal mechanism to be banked upon.

The Certified Deep Learning Expert course mainly focuses on employing the knowledge of deep learning into the organizational functions. The certification, accredited by  IABAC carries wide recognition and is instrumental in fulfilling long term career goals.

Certified Deep Learning Expert Course Training Cost




Deep Learning is an indispensable part of  Machine Learning. Every organization is making an effort towards leveraging the possibilities of deep learning. The Certified Deep Learning Expert mainly focuses on the incorporation of deep learning into the activities of an enterprise.

Given the successful completion of the course, you will be equipped with the following:-

  • Deep Learning -An Overview 

  • Applications Of Deep Learning 

  •  Machine Learning.

  • Helps to lead forward in the Data Science career trajectory 

  • No mandatory prerequisite.

  • Experience in the deep learning domain recommended.

  • Data Scientists who have a specialization in operations.

  • Professionals working in the operations domain 

  • Wide recognition for certification.

  • Higher Pay.

  • Beneficial for scaling up in the career trajectory

DataMites™ is an authorized institute by the International Association of Business Analytics Certifications (IABAC™) providing global data science certification courses

  • DataMites™ is founded by Data Science/Analytics Experts, who had deep roots in the industry in the USA and Europe for decades.

  • You benefit from course syllabus of DataMites™ as designed inline with job requirement in the current market

  • You can choose from flexible learning options: Traditional classroom, Live Virtual classroom, and Self-learning.

  • DataMites™ has a dedicated Placement Assistance Team (PAT), which assists you to find the right data science job

  • If you are a worthy candidate, you can get an opportunity to work in global data science consulting projects

  • You can treat your DataMites™ relationship manager as your pal. We value your every request.


● Machine Learning Primer

● Machine Learning core concepts, scalable algorithms, project workflow.

● Objective Functions and Regularization

● Understanding the Objective Function of ML Algorithms

● Metrics, Evaluation Methods, and Optimizers

● Popular Metrics in Detail: R2 Score, RMSE, Cross-Entropy, Precision, Recall, F1 Score, ROC-AUC, SGD,


● Artificial Neural Network

● ANN in detail, Forward Pass and Back Propagation

● Machine Learning Vs Deep Learning

● Core difference b/w ML and DL from an implementation perspective

● Python Programming Primer

● Installing Python, Programming Basics, Native Data types

● Class, Inheritance and Magic Functions

● Python Classes, Inheritance Concepts, Magic Functions

● Special Functions in Python

● Overview, Array, selecting data, Slicing, Iterating, Array Manipulations, Stacking, Splitting arrays, Key


● Decorators and Special Functions

● Decorators implementation with class

● Context Manager ‘with’ in Python

● Context Manager Application

● Exception Handling

● Try and Catch block

● Python Package Management

● Bundling and export python packages

● TensorFlow 2.0 Basics

● TensorFlow core concepts, Tensors, core APIs

Concrete Functions, Data Types, Control Statements

● Polymorphic Functions, Concrete Functions, Data Types, Control Statements, NumPy, Pandas

● Autograph eager execution

● tf.function autograph implementation

● Sessions vs tf.function

● Keras (TensorFlow 2.0 Built-in API) Overview

● Sequential Models, configuring layers, loading data, train and test, complex models, callbacks, save

and restore Neural Network weights

● Building Neural Networks in Keras

● Building Neural networks from scratch in Keras

● Implementing RNN, CNN in Keras

● Building Recurrent Neural Networks for sequence data and Convolution Neural Networks for Image


● Linear Algebra

● Vectors, Matrices, Linear Transformation, Eigen Vectors, Matrix Operations, Special Matrices

● Calculus – Derivatives: Calculus essentials, Derivatives, and Partial Derivatives, Chain Rule, Derivatives

of special functions

● Probability Essentials: Probability basics and notations, Conditional probability, Essential Probability

theorems for Machine Learning

● Special functions: Relu, Sigmoid, SoftMax, Popular Loss Functions – Cross-Entropy, Quadratic Loss


● Deep Learning Network Concepts

● Core concepts of Deep Learning Networks

● Deep Dive into Activation Functions

● Relu, Sigmoid, Tanh, SoftMax, Linear

● Building simple Deep Learning Network

● Simple DL network from starch

● Tuning Deep Learning Network

● Tuning Deep Learning Network Parameters for optimal performance, Stopping Criteria

● Visualizing Training using TensorBoard

● Visualizing Deep Learning Network using TensorBoard

● Deep Learning Architectures

● Popular Deep learning Architectures – CNN, RNN, LSTM RNN, GRU RNN Introduction

● Deep Dive into Convolutional Neural Network

● Core Concepts of Convolutional Neural Network, Feature Maps, Relu Activation, Max Pooling

● CNN Application – Image Classification

● Image Classification implementation with CNN TensorFlow 2.0 (Keras)

● Recurrent Neural Networks (RNN) Basics

RNN Architecture, BPTT Backprop through time, Mathematics of RNN


● Vanishing Gradient and exploding Gradient problem, LTSM architecture, GRU Architecture.

● LSTM RNN implementation in TensorFlow

● LSTM RNN project.

● Big Data Platforms

● Importing Big Data

● PySpark functions for importing data from various sources and other big data frameworks.

● Machine Learning with PySpark

● Implementing scalable ML models with PySpark.

● Image Classification

● Image Classification with CIFAR-10 Dataset

● Human Face Detection

● Traffic Sign Detection

● Human Activity Detection

● 20BN-something-something Dataset V2

● Image Caption Generation


DataMites™ provides flexible learning options from traditional classroom training, the latest virtual live classroom to distance course. Based on your location preference, you may have one or more learning options

This course is perfectly aligned with the current industry requirements and gives exposure to all the latest techniques and tools. The course curriculum is designed by specialists in this field and monitored and improved by industry practitioners on a continual basis.

All certificates can be validated with your unique certification number at the IABAC.org portal. You also get candidate login at exam.iabac.org, where you can find your test results and other relevant validation details.

The results of the Exam are immediate if you take an online test at the exam.iabac.org portal. The certificate issuance, as per IABAC™ terms, takes about 7-10 business days for e-certificate.

No, the exam fees are already included in the course fee and you will not be charged extra.

The course fee needs to be paid in one payment as it is required to block your seat for the entire course as well as book the certification exams with IABAC™. In case, if you have any specific constraints, your relation manager at DataMites™ shall assist you with part payment agreements

DataMites™ has a dedicated Placement Assistance Team(PAT), who work with candidates on an individual basis in assisting for the right Data Science job.

You get a 100% refund training fee if the training is not to your satisfaction but the exam fee will not be refunded as we pay to accreditation bodies. If the refund is due to your availability concerns, you may need to talk to the relationship manager and will be sorted out on case to case basis

DataMites™ provides loads of study materials, cheat sheets, data sets, videos so that you can learn and practice extensively. Along with study materials, you will get materials on job interviews, new letters with the latest information on Data Science as well as job updates.

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