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

Online

⭐⭐⭐⭐⭐ (382)

Enquire No:

+65 96141525

Next Class Starts:

15-08-2022

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

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Overview
Students Review
Advisors
Syllabus
Projects
Course Features
Course Fee
Contact Us
FAQ's

Next class starts Aug 15

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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

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.

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.

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.

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.

Kim

★★★★★

Ryan

★★★★★

Grace

★★★★★

This course has provided me a solid foundation for AI. Advisors focus on basics and advanced ML concepts also. Besides this, the implementation of these concepts was explained in a very easy way.

Joshua

★★★★★

Instructors

A master in business analytics from Indian Institute of science, proficient in Statistics, Machine Learning, AI, Deep Learning, Quantum AI, Computer Vision, Speech, Language understanding, Knowledge Graph, Conversational computing, and IoT.

Revanth GuthalaDirector & Head for Complete Data Science Department of GEEKLURN

Mr. Adhvaith Reddy is an expert Data Scientist for more than 13 years in market research domain and industries like Genpact, Kantar. He is expert in the subjects like Advanced Statistics, Predictive Analytics, Machine learning , Data Pre-processing, NLP and Deep Learning and Tools like Excel, SPSS, R, Python, Quantum, MiniTab, Tableau. He believes that "No prior knowledge is required to join his classes."

Mr.AdhvaithSr.Data Scientist with more than 13 years of experience in market research domain and industries like GenPact & Kantar

An enthusiastic machine learning expert with more than 10years of experience in Data Science and a skilled mentor who believes in extremely hands-on and incredibly practical teaching.

BharathSr.Data Scientist @ Accenture

With about 15 years of experience in Information Technology, He has excelled in AI Architect Designing analytics-based process automation solutions, NLP & Image Analytics Enabling, Machine Learning Engineering for predictive analytics, Advanced Python & SAS programming for ML/Deep Learning/NLP algorithms.

AbhiramSr.Data Scientist @ Amazon

Revanth Guthala

Director & Head for Complete Data Science Department of GEEKLURN

Mr.Adhvaith

Sr.Data Scientist with more than 13 years of experience in market research domain and industries like GenPact & Kantar

Bharath

Sr.Data Scientist @ Accenture

An enthusiastic machine learning expert with more than 10years of experience in Data Science and a skilled mentor who believes in extremely hands-on and incredibly practical teaching.

Abhiram

Sr.Data Scientist @ Amazon

Syllabus

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!

Projects

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.

+65 96141525