Artificial Intelligence Specialist Course

Master in-demand skills such as Deep Learning, NLP, Reinforcement Learning and multiple programming tools. Start your ML and AI journey today to become a certified Artificial Intelligence Specialist.

12 Months


⭐⭐⭐⭐⭐ (382)

Enquire No:

+65 96141525

Next Class Starts:


2200+ Alumni Students Upto $4000 Stifend for R&D Projects 100% Online Live Training Sessions 105+ Hiring Partners  Curriculum designed by Industry Experts

2200+ Alumni Students Upto $4000 Stifend for R&D Projects 100% Online Live Training Sessions 105+ Hiring Partners  Curriculum designed by Industry Experts


Next class starts Aug 15

Course Overview

12-Month Course Duration

- 6 months of live and interactive sessions by Data Experts
- 6 months of research and development projects at Geeklurn AI Solution

320+ hours of Live Training Interactive Sessions by Data Scientists

Scholarships up to $4000 on the type of Research Project undertaken

100% Placement Assistance

Placement Opportunities with our partners after course completion

Why Artificial Intelligence?

Artificial Intelligence forms the very basis of all computer learning. The amount of data generated by humans as well as machines surpasses the human ability to analyse, interpret and make complex decisions based on that data. Artificial Intelligence is the future of all decision making. Machines running on artificial intelligence are nearly influencing every facet of our lives, improving efficiencies to help us augment our human capabilities.
A person working as an Artificial Intelligence Developer in South Asia, typically earns around $8,980 per month. Salaries range from $4,490 (lowest) to $13,900 (highest).
As per a report by Accenture, published in the 2020 Emerging Jobs Report Singapore, by 2035 Artificial Intelligence is expected to add up to US$215 billion to businesses. This explains the importance of learning AI so as to change the market’s trends.
Why Geeklurn’s Artificial intelligence(AI) Course?

The Artificial intelligence program by Geeklurn is thoroughly orchestrated by AI specialists and data science experts in the industry. This curriculum is designed in a way which makes it an easy learning experience for freshers as well as experienced professionals.


This course provides in-demand skills such as Deep Learning, Reinforcement Learning, working on real time industry projects & multiple programming tools.

This technology is strongly influencing consumer products and has led to significant breakthroughs in the healthcare industry. A machine can literally do anything that a human brain does. And this is exactly what AI is all about.


There are different types of artificial intelligence. Machine learning being the most popular one amongst them, is a study where instead of getting programmed what to think, machines can observe, analyse, interpret and learn from the data and mistakes just like a human brain.

Who needs to pursue this course?

Irrespective of what educational background you come from, you can pursue this course to become an AI Specialist if you fulfill the following prerequisites:


  • Strong hold on Mathematics and Computer Science.
  • Good command over programming languages.
  • Good Analytical Skills.
  • Ability to understand complex algorithms.
  • Basic knowledge of Statistics and modeling.

What are the possible roles and job descriptions possible after this course?

  • Machine Learning Engineer
  • Data Scientist
  • Research Scientist
  • Business Intelligence Developer
  • Big Data Engineer / Architect

Students Reviews

Business Intelligence Developer@ SleekDigital
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The Geeklurn Artificial Intelligence Specialist course experience is great for those who are keen about moving in to the domain of AI. The courses content dealt with the major and fundamental concepts of ML which I was really interesting.
Research Scientist @ UVIK Software
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The Artificial Intelligence Specialist course by Geeklurn helped me learn AI from scratch and now I can understand algorithms easily. It helped me boost my career by developing the right skillset.
Big Data Engineer @ SleekDigital
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This course has provided me a solid foundation for AI. Instructors focus on basics and advanced ML concepts also. Besides this, the implementation of these concepts was explained in a very easy way.
Business Analyst @ ExpertsFromIndia
Read More
The Artificial Intelligence Specialist course by Geeklurn gave me a comprehensive introduction to AI. Through this training I learnt why AI is the new technological revolution. I highly recommend this course to anyone wanting to learn AI.

Students Reviews




Introduction to Data science & AI

  • Business Intelligence Vs Data AnalysisVs Data Scientist
  • Data Scientist Roles
  • Different disciplines of data science
    • Machine Learning 
    • Natural Language Processing 
    • Deep Learning
  • Applications of Machine Learning
  • Why Machine Learning is the Future
  • What are prerequisites for Data Science
    • Statistics
    • Python essentials for Data Science
  • Different Python modules used for Data Science

Introduction to Python

  • Overview of Python
  • Different Applications where Python is used
  • Fundamentals of Python programming 
  • Values, Types, Variables
  • Conditional Statments
  • The Companies using Python
  • Discuss Python Scripts on UNIX/Windows
  • Operands and Expressions
  • Loops
  • Writing to the screen
  • Creating the "Hello World" Code
  • Demonstrating Conditional Statements
  • Demonstrating Loops

Deep Dive - Functions, OOPs, Modules, Errors and Exceptions

  • Functions
  • Function Parameters
  • Syntax, Arguments, Keyword Arguments, Return Values
  • Global Variables
  • Lambda Functions
  • Features, Syntax, Options, Compared with the Functions
  • Standard Libraries
  • Variable Scope and Returning Values
  • Object-Oriented Concepts
  • Modules Used in Python
  • Module Search Path
  • Errors and Exception Handling
  • Types of Issues, Remediation

Data Manipulation

  • Basic Functiondlities of a data object
  • Concatenation of data objects
  • Exploring a Dataset
  • Merging of Data objects
  • Types of Joins on data objects
  • Aggregation
    • Merging
    • GroupBy operations
    • Concatenation
    • Joining
  • Analysing a dataset
  • Pandas Function- Ndim(), axes(), values,
  • head(), tail(, sum(), std), iteritems(), 
  • iterrowsy), itertuples()

Introduction to Machine Learning with Python

  • Python Revision (numpy, Pandas, scikit learn, matplotlib)
  • Machine Learning Use-Cases
  • Machine Learning Categories
  • Gradient Descent
  • What is Machine Learning?
  • Machine Learning Process Flow
  • Machine Learning types
  • Linear regression
  • Linear Regression Implementation

Supervised Learning - 1

  • Implementing different types of Supervised Learning algorithms
  • What are Classification and its use cases?
  • Confusion Matrix
  • Evaluating model output
  • Implementation of Logistic regression
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Perfect Decision Tree
  • Wh a t is a Random Forest?

Dimensionality Reduction

  • Introduction to Dimensionality
  • Why Dimensionality Reduction
  • Implementing Dimensionality Reduction 
  • Technique. 
  • PCA
  • Scaling dimensional model
  • Factor Analysis
  • LDA
  • Supervised Learning - II
  • Supervised Learning concepts
  • What is Naive Bayes?
  • How Naive Bayes works?
  • Implementing Naive Bayes Classifier
  • What is Support Vector Machine?
  • How SVM works?
  • Implementation of Support Vector Machine
  • Grid Search vs Random Search
  • Hyperparameter Optimization
  • Implementation of Support Vector Machine for Classification 
  • Unsupervised Learning
  • What is Clustering & its Use Cases?
  • What is K-means Clustering?
  • How does the K-means algorithm work?
  • Implementing K-means Clustering
  • What is C-means Clustering?
  • Implementation of Clustering - various 
  • types. 
  • What is Hierarchical Clustering?
  • Implementing Hierarchical Clustering
  • How to do optimal clustering

Unsupervised Learning

  • What is Clustering & its Use Cases?
  • What is K-means Clustering?
  • How does the K-means algorithm work?
  • Implementing K-means Clustering
  • What is C-means Clustering?
  • Implementation of Clustering - various 
  • types. 
  • What is Hierarchical Clustering?
  • Implementing Hierarchical Clustering
  • How to do optimal clustering

Association Rules Mining and Recommendation Systems

  • What are Association Rules?
  • Calculating Association Rule Parameters
  • Association Rule Parameters
  • How does Recommendation Engines work?
  • Content-Based Filtering
  • Collaborative Filtering
  • Market Basket Analysis
  • Apriori Algorithm

Reinforcement Learning

  • What is Reinforcement Learning
  • Elements of Reinforcement Learning
  • Why Reinforcement Learning
  • Implement Reinforcement Learning using 
  • Python. 
  • Epsilon Greedy Algorithm
  • Q values and V values and A values
  • Q- Learning
  • Implementing Q Learning
  • Developing Q Learning model in Python
  • Exploration vs Exploitation dilemma
  • Markov Decision Process (MDP)
  • Calculating Reward
  • Calculating Optimal quantities
  • Setting up an Optimal Action
  • Discounted Reward
  • Implementing Q Learning
  • Introduction to NumPy, Pandas and Matplotlib
  • Operations on arrays
  • Reading and writing arrays on files
  • Reading and Writing data from Excel/CSV formats into Pandas.
  • NumPy- arrays
  • NumPy library- Creating NumPy array,
  • operations performed on NumPy array.
  • Matplotlib - Using Scatterplot, histogram,
  • bar graph, a pie chart to show information,
  • Styling of Plot.
  • Types of plots - bar graphs, pie charts,
  • histograms, counter plot. 
  • Pandas - data structures & index operations
  • Pandas library- Creating series and data 
  • frames, Importing and exporting data.

Natural Language Processing with deep Learning in Python

  • Understand and implement word2vec
  • Understand the CBOW method in word2vec
  • Understand the skip-gram method in word2vec
  • Understand the negative sampling optimisation in word2vec
  • Understand and implement GloVe using gradient descent and alternating least squares
  • Use recurrent neural networks for parts-of-speech tagging
  • Use recurrent neural networks for named entity recognition
  • Understand and implement recursive neural networks for sentiment analysis
  • Understand and implement recursive neural tensor networks for sentiment analysis

Natural Language Processing in TensorFlow

  • Build natural language processing systems using TensorFlow
  • Process text, including tokenisation and representing sentences as vectors
  • Apply RNNs, GRUS, and LSTMs in TensorFlow
  • Train LSTMs on existing text to create original poetry and more

Module 1 : Introduction to Natural Language Processing

  • Introduction to Natural Language Processing
  • Exercise: Introduction to Natural Language Processing 
  • Podcast with NLP Researcher Sebastian Ruder

Module 2 : A Refresher to Python

  • Installation steps for Linux 
  • Installation steps for Mac 
  • Installation steps for Windows
  • Packages Installation
  • Introduction to Python
  • Variables and Operators
  • Exercise: Variables and Operators
  • Python Lists 
  • Exercise: Python Lists
  • Dictionaries 
  • Exercise: Dictionaries
  • Conditional Statements
  • Loops
  • Exercise: Loops
  • Functions
  • Python Functions Practice
  • Exercise: Functions
  • Packages
  • Exercise: Packages
  • Files 
  • Exercise: Files

Module 3 : Learn to use Regular Expressions

  • Understanding Regular Expression
  • Implementing Regular Expression in Python
  • Exercise: Implementing Regular Expression in Python
  • Regular Expressions in Action

Module 4 : First Step of NLP - Text Processing

  • Tokenization and Text Normalisation
  • Exercise: Tokenisation and Text Normalisation
  • Exploring Text Data
  • Part of Speech Tagging and Grammar Parsing 
  • Exercise: Part of Speech Tagging and Grammar Parsing
  • Implementing Text Pre-processing Using NLTK 
  • Exercise: Implementing Text Pre-processing Using NLTK
  • Natural Language Processing Techniques using spaCy

Module 5 : Extracting Named Entities from Text

  • Understanding Named Entity Recognition
  • Exercise: Understanding Named Entity Recognition
  • Implementing Named Entity Recognition 
  • Exercise: Implementing Named Entity Recognition
  • Named Entity Recognition and POS tagging using spaCy
  • POS and NER in Action: Text Data Augmentation

Module 6 : Feature Engineering for Text

  • Introduction to Text Feature Engineering
  • Count Vector, TFIDF Representations of Text
  • Exercise: Introduction to Text Feature Engineering
  • Understanding Vector Representation of Text 
  • Exercise: Understanding Vector Representation of Text
  • Understanding Word Embeddings
  • Word Embeddings in Action - Word2Vec
  • Word Embeddings in Action - Glove

Module 7 : Mastering the Art of Text Cleaning

  • Introduction to Text Cleaning Techniques Part 1
  • Exercise: Introduction to Text Cleaning Techniques Part 1
  • Introduction to Text Cleaning Techniques Part 2
  • Exercise: Introduction to Text Cleaning Techniques Part 2
  • Text Cleaning Implementation
  • Exercise: Text Cleaning Implementation
  • NLP Techniques using spaCy

Module 8 : Interpreting Patterns from Text - Topic Modelling

  • Introduction to Topic Modelling 
  • Exercise: Introduction to Topic Modelling
  • Understanding LDA
  • Exercise: Understanding LDA
  • Implementation of Topic Modelling
  • LSA for Topic Modelling

Module 9 : Machine Learning Algorithms

  • Types of Machine Learning Algorithms
  • Logistic Regression
  • Decision Tree
  • Naive Bayes
  • SVM (Support Vector Machine)
  • Random Forest

Module 10 : Understanding Text Classification

  • Overview of Text Classification
  • Assignment: Share your learning and build your profile

Module 11 : Introduction to Deep Learning

  • Getting started with Neural Network
  • Exercise: Getting started with Neural Network
  • Understanding Forward Propagation
  • Exercise: Forward Propagation
  • Math Behind forwarding Propagation 
  • Exercise: Math Behind forwarding Propagation
  • Error and Reason for Error
  • Exercise: Error and Reason for Error
  • Gradient Descent Intuition
  • Optimiser
  • Back Propagation
  • Why Keras?
  • Building a Neural Network for Text Classification
  • Why CNN?
  • Understanding the working of CNN Filters
  • Padding Strategies
  • Padding Strategies in Keras
  • Introduction to Pooling
  • CNN architecture and it's working

Module 12 : Deep Learning for NLP

  • Deep Learning for NLP Part 1
  • Deep Learning for NLP Part 2
  • Text Generation Using LSTM

Module 13: Recurrent Neural Networks

  • Why RNN
  • Introduction to RNN: Shortcomings of an MLP
  • Introduction to RNN: RNN Architecture
  • Training an RNN: Forward propagation 
  • Training an RNN: Backpropagation through time
  • Need for LSTM/GRU
  • Long Short Term Memory (LSTM)
  • Gated Recurrent Unit (GRU)

Module 14 : Introduction to Language Modelling in NLP

  • Overview: Language Modelling
  • What is a Language Model in NLP?
  • N-gram Language Model
  • Implementing an N- gram Language Model - |
  • Implementing an N-gram Language Model - II
  • Neural Language Model
  • Implementing a Neural Language Model

Module 15 : Sequence-to-Sequence Modelling

  • Intuition Behind Sequence-to-Sequence Modelling
  • Need for Sequence-to-Sequence Modelling
  • Understanding the Architecture of Sequence-to-Sequence
  • Understanding the Functioning of Encoder and Decoder
  • Case Study: Building a Spanish to English Machine Translation Model
  • Preprocessing of Text Data
  • Converting Text to Integer Sequences
  • Model Building and Inference

Module 16 : Bonus Section (Advance NLP tools)

  • Text Classification & amp; Word Representations using FastText (An NLP library by Facebook)
  • Introduction to Flair for NLP: A Simple yet Powerful State-of-the-Art NLP Library
  • Introduction to Stanford NLP: An Incredible State-of-the-Art NLP Library for 53 Languages with Python code
  • A Step-by-Step NLP Guide to Learn Elmo for Extracting Features from Text
  • Tutorial on Text Classification (NLP) using ULMFIT and fastai Library in Python
  • 8 Excellent Pretrained Models to get you started with Natural Language Processing (NLP)
  • Data Science Project Management 
    • Leading Data Science Teams & Processes
    • Exploring Methodologies
    • How to manage data science projects and lead a data science team
    • Agile Data Science
    • Scrum Data Science
    • Emerging Approaches – Microsoft TDSP
    • Data Science Methodology understanding
    • Business & Data understanding
    • Modelling & Evaluation
    • Plan Deployment
    • Data Science Project Report

Hello, world!


Analytics Industry
Sentiment Analysis on Twitter data regarding 2020 INDIAN Elections
Health Care/ Pharma Industry
AI Doctor Now Diagnoses Disease Better Than Your Doctor, Study Finds
Chip Design / Semiconductor Industry
Applying Artificial Neural Network to Predict Semiconductor Machine Outliers
Cloud Computing Industry
Fault Tolerance & Redundant System with Seamless Integration to Development on AWS
Financial Services & Software
Robo-Advisors common place in the financial domain
Agriculture Sector
Fight Food Scarcity and Empower Small Farmers

Course Features

Mentorship by Industry Experts

Professional guidance on courses related doubts from our industrial mentors.

Industrial Boost camps

Participate in hackathons, live research and development projects and online sessions.

Peer Networking

Exchange queries, project ideas, knowledge with our alumni, experts and your colleagues.

Placement Assistance

Get corporate guidance from our experienced mentors who help you get job-ready.

Course Fees

Online Training + Mentorship

$1000 USD

Apply Now

Frequently Asked Questions

What are some examples of AI in use today

  • Chatbots.
  • Facial recognition.
  • Image tagging.
  • Natural language processing.
  • Sales prediction.
  • Self-driving cars.
  • Sentiment analysis.

What do we mean by AI neural networks?

Basically, the neural networks in AI mathematically model how the human brain works. This approach enables the machine to think and learn just exactly as humans do. This is how smart technology today recognizes speech, objects, and much more.

Can I have access to the live training sessions in case I missed one?

We have a 24x7 LMS access for all our live online classes.

Do we have an EMI option for making payments ?

Students have the leverage to buy courses using credit card EMI.

Do we have any R&D projects for this course?

We offer a six months internship for every course after the live training sessions for the first six months. During the course of the internship, you will be exposed to industry knowledge, corporate sector, research and development and hands-on practical experience by our experts.