Deep Learning for
Artificial Intelligence
Live Training Led by Tech Masters!
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.
Schedule
Starting
05 November ’22
Duration
8 Weeks
Timings
9:00 AM – 1:00 PM
(Sat & Sun only)
Language
Urdu/Hindi
Meet the Instructor!
Meet the trainer of this course who is an Artificial Intelligence Expert!
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
- Individual Price
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PKR 30,000 Per Person
- Total charges for complete training
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- Group of Two
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PKR 27,900 Per Person
- Avail 7% off on Group of two
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- Group of Three
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PKR 27,000 Per Person
- Avail 10% off on Group of three
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- Group of Four or more
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PKR 25,500 Per Person
- Avail 15% off on Group of four or more
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Reserve your Seat
You can reserve your seat by filling the form below!
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: 09AM – 1PM
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.