No. of Hours: 54

Certificate Validity: 2 Yr(s)


  • Knowledge of Python, familiarity with control flow (if/else, for loops) and pythonic constructs (functions, classes, iterables, generators)
  • Use of bash shell (or equivalent command prompt) and basic commands to copy and move files
  • Basic knowledge of linear algebra (what is a vector, what is a matrix, how to calculate dot product)

Course Outcome

  • To describe what Deep Learning is in a simple yet accurate way
  • To explain how deep learning can be used to build predictive models
  • To distinguish which practical applications can benefit from deep learning
  • To install and use Python and Keras to build deep learning models
  • To apply deep learning to solve supervised and unsupervised learning problems involving images, text, sound, time series and tabular data.
  • To build, train and use fully connected, convolutional and recurrent neural networks
  • To look at the internals of a deep learning model without intimidation and with the ability to tweak its parameters
  • To estimate training costs for large models
  • To re-use pre-trained models to shortcut training time and cost (transfer learning)

Target Audience

  • Software engineers who are curious about data science and about the Deep Learning buzz and want to get a better understanding of it.
  • Data scientists who are familiar with Machine Learning and want to develop a strong foundational knowledge of deep learning.

Module – 1

– Tabular data
– Data exploration with Pandas code along
– Visual data Exploration
– Plotting with Matplotlib
– Unstructured Data
– Images and Sound in Jupyter
– Exercises
Machine Learning
– Machine Learning Problems
– Supervised Learning
– Linear Regression

Module – 2

Machine Learning
– Cost Function
– Cost Function code along
– Finding the best model
– Linear Regression code along
– Evaluating Performance
– Evaluating Performance code along
– Classification
– Classification code along
– Overfitting
– Cross Validation
– Cross Validation code along
– Confusion matrix
– Confusion Matrix code along
– Feature Preprocessing code along

Module – 3

Deep Learning Intro
– Deep Learning successes
– Neural Networks
– Deeper Networks
– Neural Networks code along
– Multiple Outputs
– Multiclass classification code along
– Activation Functions
Gradient Descent
– Derivatives and Gradient
– Back-propagation intuition
– Chain Rule
– Derivative Calculation
– Fully Connected Backpropagation
– Matrix Notation
– Numpy Arrays code along
– Learning Rate
– Learning Rate code along
– Gradient Descent
– Gradient Descent code along
– Optimizers

Module – 4

Convolutional Neural Networks
– Features from Pixels
– MNIST Classification
– MNIST Classification code along
– Beyond Pixels
– Images as Tensors
– Tensor Math code along
– Convolution in 1 D
– Convolution in 1 D code along
– Convolution in 2 D
– Image Filters code along
– Convolutional Layers
– Convolutional Layers code along
– Pooling Layers
– Pooling Layers code along
– Convolutional Neural Networks
– Convolutional Neural Networks code along
– Weights in CNNs
– Beyond Images

Module – 5

Recurrent Neural Networks
– Time Series
– Sequence problems
– Vanilla RNN
– LSTM and GRU
– Time Series Forecasting code along
– Time Series Forecasting with LSTM code along
– Rolling Windows
– Rolling Windows code along
Case Studies

1. Operating System: Ubuntu 18.04 LTS
2. RAM: 4 to 6 GB
3. Processor: i3 (Minimum) with Graphics Card

6 Hrs per Week (Saturday and Sunday) : Weekend Batch and
6 Hrs per Week (Wed, Thu and Fri) : Weekdays Batch
2 Months

Weekly Assignment:
1. After every weekend session you’ll need to solve assignments until next week.
2. On completion of 1 module, you are required to appear exam.
3. All exams will be taken either on Center / Off site. When Off-site you will submit assignments via mail.

Final Exam:
A duration of 15 days, will be given after completion of Course
The Final exam will be a conducted once, in case of Failure; exam can be reappeared after another 1 month

As first Opportunity after Passing exams, you will deployed on a Well monitored Live Project.
You are required to resolve the Problem statement with Expected outputs, with that you will be receiving a token of Completion Letter of respective Project.