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Advanced Machine Learning

Live Training Led by Tech Masters!

During this Interactive Live training on Advanced Machine Learning 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

28th August 2021

Duration

8 Weeks

Timings

11:00 AM – 4:00 PM

Language

Urdu/Hindi

Instructor

Meet the trainer of this course who is Advance Machine Learning expert!

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Mr. Albab Ahmad Khan

Deep Learning Engineer | Data Scientist

Mr. Albab has been working as Data Scientist for Telecom Sector, Government Organizations and Research Labs. He is the author of various International Conference Publications as well as Journal Publications in Data Science, Machine Learning and Biomedical Image Processing. His key expertise are in domains of Machine Learning, Deep Learning, Data Mining, Data Driven Marketing and Data Prediction.

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: NLP, Face Generation & Deep Fake Era

  • NLP Basics
  • Gated Recurrent Units (GRUs)
  • Glove & TFIDF
  • Word2Vector
  • Word Embeddings
  • Word Representations
  • Sentiment Analysis
  • Sentiment Prediction with CNNs
  • Sentiment Prediction with RNNs
  • Chatbot Development
  • Defining Intents in Chatbots
  • Data Preparation for Chatbot
  • Sequential Model Class of Keras
  • Chatbot Validation and Integration
  • 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
    • PKR 22,000 Per Person
    • Total charges for complete training
    • Book a seat
  • Group of Two
  • Group of Three

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?

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.

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!

     
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