Deep Learning for
Artificial Intelligence

Live Training Led by Tech Masters!

During this Interactive Live training on Deep Learning for Artificial Intelligence you will learn to make Algorithms in Python from our Machine Learning & Data Science experts who are highly sought after skills in tech and gets the hands-on experience which will prepare and equip you with the in-demand skills required to become a successful AI practitioner.

You will be practicing all the applications on Python, TensorFlow & the tools which our experts will be teaching you. After finishing this specialization, you will likely find creative ways to apply it to your work & add the certification to your professional portfolio to attract future employers.

What will you learn?

You’ll understand how to build neural networks

You’ll learn how to lead machine learning projects.

You’ll implement deep neural network for the task of image captioning.

You’ll build, train, and deploy different types of deep learning architectures.

You’ll understand the algorithms in detail.

You’ll also use Keras, Python, TensorFlow, Jupyter & Anaconda to develop, train, load and test the Deep Learning models.

You’ll develop intuition about the logics making all the magic happen.

View Course Outline

Our Approach for ZOOM Interactive Classes

After establishing a reputable Physical Training Model, based on our stellar records and customer earned trust we gradually progressed to establish the same reputation in the Live Training Model. We are committed to empowering you by making our trainings accessible, interactive, and well-curated specifically to your objective.

live training process 2

Schedule

Starting

29 October ’22

Duration

8 Weeks

Timings

9:00 AM – 1:00 PM

Language

Urdu/Hindi

Meet the Instructor!

Meet the trainer of this course who is an Artificial Intelligence Expert!

Mr. Hassan Aftab Mughal

Lead Data Scientist @ Telenor

Mr. Hassan Aftab is currently working as Manager Contextual Marketing in Telecom Industry. He has 6+ years of working experience in Top Telecom and FinTech Companies. Hassan had pivotal role in delivering crucial business solutions and completed several Data Science Certifications to upskill his knowledge in this domain. He has accomplished Associate's degree in AI & ML from Udacity and many other certifications from LinkedIn Learning Platform.

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Tools & Libraries

Course Outline

Below is the detailed outline with weekly themes

Module 0 : Foundation Week (For those who don't know Python)

Interactive Discussions on Assignment
Types of Variables
Data Visualizations (Scatter plot, Histogram, Bar plots, Line plots, Heat maps)
Data Centricity (Mean, Modes, Median, STD, Variance, Interquantile Range).
Data Transformation (Log, Natural Log, Min Max )
Data Cleaning in Python
Visualization on Matplotlib
Basics of Data Science Flow
Anaconda Installation
Intro to Jupyter Notebook
Intro to Python
Python Objects & Data Structure
Subsetting (Strings, Lists, Dictionaries)
Python Comparison Operators
Python Statements
Methods & Functions
Importing Data in Python
Pandas Basics in Python
Subsetting Dataframes in Pandas
Data Aggregation using Group By, Pivot, and Melt
Hands-On Assignment of Python

Module 1: Python Takeoff & Data Wrangling

What is Anaconda
Jupyter Notebook Overview
Introduction to Data Science
Python Basics
Python Objects
Python Objects and Data Structures
Subsetting (String, Lists, Dictionaries)
Python Comparison Operators
Python Statements
Python Methods & Functions
Importing Data in Python (CSV, Excel, JSON, XML)
Numpy Basics
Pandas Basics
Numpy & Pandas comparison, when to use what
What is Data Aggregation & Data Pivoting?
Group By, Pivot & Melt in Python

Module 2 : Data Visualization and Exploration in Python

Understand Visualization Types
When to Use Which Visualization
Histograms
Scatter Plots
Bar Plots
Line Plots
Heat Maps
Geo Maps
Data Centrality and Data Distribution Types
Mean, Modes, Median, STD, Variance, Interquartile Range, Box Plots
Play with Data Transformations
Log Transformations
Natural Log Transformations
Min Max Transformations
Matplotlib Library in Python
Seaborn Library in Python
Feature Engineering
Missing Values handling
Correlation Matrix

Module 3: Deep Learning Kickoff

Neural Networks Magic
What is Deep Learning
Deep Learning Use Cases in Real Life
Deep Learning Frameworks (Tensor Flow, Keras, Pytorch)
Tensor Flow Introduction
Tensors Concept in Python (Numpy)
Optimization with Gradient Descent
Biological & Mathematical Neurons
Perceptron Understanding
Shallow Neural Networks
Deep Neural Networks
Neural Networks Mathematics Behind the Wall
Loss Functions
Regularization (L1 & L2)
Hyperparameters Tuning
Metrics Foundations
Optimizer Selection (AdaGrad, RMSprop, Adam, SGD)
Binary & Categorical Cross Entropy
Forward Propagation
Backward Propagation
Chain Rule
Weights Adjustments
Activations Functions

Module 4: Computer Vision Arena

Computer vision foundations
What is Computer Vision in AI
Edge Detection
GreyScale & RGB Images Analysis Techniques
Image Classification with Keras
Error Analysis
Model Complexity
Overfitting & Underfitting Challenge
Dropout & Learning Rate
Vanishing Gradient & Momentum
Convolutional Concept
Padding & Strides
Feature Maps
Non-Linearity in CNNs
Max, Min, Average Pooling

Module 5: Transfer Learning, Share AI Knowledge

Visualization of CNNs
How to visualize a Neural Network
Layer on Layer Feature Maps Understanding
Transfer Learning Basics
ImageNet Large Scale Computer Vision Data
AlexNet Architecture
Visual Geometry Group Architecture (VGG16, VGG19)
Inception V3 Architecture
Exception Architecture
ResNet 50 Architecture
Weight Initialization
Auto Encoders

Module 6: Object Detection & Face Recognition

Object Detection
Single Object Localization
RCNN Model Family
Faster-RCNN
Yolo Model Family
One Short Learning
Bounding Box Prediction
Intersection Over Union
Non-Max Suppression
Convolutional Implementation of Sliding Windows
Anchor Boxes
Face Recognition
Open CV
Face Matching
Face Similarity
Face Transformation
Face Identification
Advanced Face Recognition
Deep Face Recognition
Face Recognition with VGGFace2
Triplet Loss
Siamese Networks

Module 7: Handle Timeseries in Deep Learning

Applications of RNNS around us
Time Series Data Understanding
Speech Recognition
Time Series Prediction
Gesture Recognition
Natural Language Processing
Recurrent Neural Networks
Time Delay Neural Networks (TDNN)
ELMAN Network
Folded & Unfolded Models
Backpropagation through Time (BPPTT)
Gradient Clipping
Gradient Exploding
Long Short term memory
LSTM Understanding
Learn Gate
Forget Gate
Remember Gate
Use Gate
Character Wise RNNs Implementation

Module 8: Face Generation & Deep Fake Era

Generative Models
Supervised vs Unsupervised
Discriminative vs Generative Modeling
Generative Models
Generative Adversarial Networks
Generator Model
Discriminator Model
GANs & CNN
PIX2PIX & CYCLEGAN
Face Generation
Deep Fake Introduction

Pricing Details

Online Banking details will be shared by our representatives after you reserve your seat

Actual Training Price
- PKR 30,000 Per Person
- Total charges for complete training
- Book a seat

For [email protected] Members
- PKR 24,000 Per Person
- 20% off for [email protected] members
- Register Now

Frequently Asked Questions

Who should attend the course?

Graduate or Masters Students with IT, CS or AI background who want to start their career in the Artificial Intelligence & Data Science domain

People who are working in the Data Analytics, AI & Data Science domain and want to advance their career

Executive who want to build a Data Analytics & AI department in their start-ups/organizations

What is the timing of the course?

Duration: 8 weeks (Saturdays & Sundays)
Timings: 11AM – 4PM

Who are the Instructors?

Mr. Hassan Aftab (Expert Data Scientist @Telenor)

How much hands-on will be performed in this course?

Since our courses are led by Industry Experts so it is made sure that content covered in course is designed with hand on knowledge of more than 70-75 % along with supporting theory.

What are the PC requirements?

For Advanced Machine Learning course, you need to have a PC with minimum 4GB RAM.

What if I miss any of the lectures?

Don’t worry! We have got you covered. You shall be shared recorded lectures after each session, in case you want to revise your concepts or miss the lecture due to some personal or professional commitment.

How will this training ensure hands-on practice?

For executing the practical’s included in the AML Training, you will set-up tool on your machine. The installation manual for tool prep will be provided to help you install and set-up the required environment.

Can I rejoin this workshop/training?

Yes, you can rejoin the training within the span of an year of your registration. Please note following conditions in case you’re rejoining.
1) There are only 5 seats specified for rejoiners in each iteration.
2) These seats will be served on first come first basis.
3) If you have not submitted your complete fee, you may not be able to rejoin. Your registration would be canceled.

What sort of projects will be part of this Live Training?

This Certification Training course includes multiple real-time, industry-based projects, which will hone your skills as per current industry standards and prepare you for the future career needs.

Will I get a certificate after this course?

Yes, you will be awarded with a course completion certificate by Dice Analytics. We also keenly conduct an annual convocation for the appreciation and recognition of our students.

Can I get a job after this course?

Since our instructors are industry experts so they do train the students about practical world and also recommend the shinning students in industry for relevant positions.

Reserve your Seat

You can reserve your seat by filling the form below!

Deep Learning forArtificial Intelligence