Artificial Intelligence Specialist Course
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
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?


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.
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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.
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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.
Irrespective of what educational background you come from, you can pursue this course to become an AI Specialist if you fulfill the following prerequisites:
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- 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.
- Machine Learning Engineer
- Data Scientist
- Research Scientist
- Business Intelligence Developer
- Big Data Engineer / Architect
Students Reviews




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 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.

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.

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.
Instructors








Advisors








Syllabus
- Business Intelligence Vs Data AnalysisVs Data Scientist
- Data Scientist Roles
- Different disciplines of data science
- Machine LearningÂ
- Natural Language ProcessingÂ
- 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
- Overview of Python
- Different Applications where Python is used
- Fundamentals of Python programmingÂ
- 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
- 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
- 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(),Â
- iterrowsy), itertuples()
- 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
- 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?
- Introduction to Dimensionality
- Why Dimensionality Reduction
- Implementing Dimensionality ReductionÂ
- Technique.Â
- 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 ClassificationÂ
- 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 - variousÂ
- types.Â
- What is Hierarchical Clustering?
- Implementing Hierarchical Clustering
- How to do optimal clustering
- 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 - variousÂ
- types.Â
- What is Hierarchical Clustering?
- Implementing Hierarchical Clustering
- How to do optimal clustering
- 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
- What is Reinforcement Learning
- Elements of Reinforcement Learning
- Why Reinforcement Learning
- Implement Reinforcement Learning usingÂ
- Python.Â
- 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.Â
- Pandas - data structures & index operations
- Pandas library- Creating series and dataÂ
- frames, Importing and exporting data.
- 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
- 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
- Introduction to Natural Language Processing
- Exercise: Introduction to Natural Language ProcessingÂ
- Podcast with NLP Researcher Sebastian Ruder
- Installation steps for LinuxÂ
- Installation steps for MacÂ
- Installation steps for Windows
- Packages Installation
- Introduction to Python
- Variables and Operators
- Exercise: Variables and Operators
- Python ListsÂ
- Exercise: Python Lists
- DictionariesÂ
- Exercise: Dictionaries
- Conditional Statements
- Loops
- Exercise: Loops
- Functions
- Python Functions Practice
- Exercise: Functions
- Packages
- Exercise: Packages
- FilesÂ
- Exercise: Files
- Understanding Regular Expression
- Implementing Regular Expression in Python
- Exercise: Implementing Regular Expression in Python
- Regular Expressions in Action
- Tokenization and Text Normalisation
- Exercise: Tokenisation and Text Normalisation
- Exploring Text Data
- Part of Speech Tagging and Grammar ParsingÂ
- Exercise: Part of Speech Tagging and Grammar Parsing
- Implementing Text Pre-processing Using NLTKÂ
- Exercise: Implementing Text Pre-processing Using NLTK
- Natural Language Processing Techniques using spaCy
- Understanding Named Entity Recognition
- Exercise: Understanding Named Entity Recognition
- Implementing Named Entity RecognitionÂ
- Exercise: Implementing Named Entity Recognition
- Named Entity Recognition and POS tagging using spaCy
- POS and NER in Action: Text Data Augmentation
- Introduction to Text Feature Engineering
- Count Vector, TFIDF Representations of Text
- Exercise: Introduction to Text Feature Engineering
- Understanding Vector Representation of TextÂ
- Exercise: Understanding Vector Representation of Text
- Understanding Word Embeddings
- Word Embeddings in Action - Word2Vec
- Word Embeddings in Action - Glove
- 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
- Introduction to Topic ModellingÂ
- Exercise: Introduction to Topic Modelling
- Understanding LDA
- Exercise: Understanding LDA
- Implementation of Topic Modelling
- LSA for Topic Modelling
- Types of Machine Learning Algorithms
- Logistic Regression
- Decision Tree
- Naive Bayes
- SVM (Support Vector Machine)
- Random Forest
- Overview of Text Classification
- Assignment: Share your learning and build your profile
- Getting started with Neural Network
- Exercise: Getting started with Neural Network
- Understanding Forward Propagation
- Exercise: Forward Propagation
- Math Behind forwarding PropagationÂ
- 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
- Deep Learning for NLP Part 1
- Deep Learning for NLP Part 2
- Text Generation Using LSTM
- Why RNN
- Introduction to RNN: Shortcomings of an MLP
- Introduction to RNN: RNN Architecture
- Training an RNN: Forward propagationÂ
- Training an RNN: Backpropagation through time
- Need for LSTM/GRU
- Long Short Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
- 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
- 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
- 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 ManagementÂ
- 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
Projects
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
- 12-Month Course Duration
- 6 months of Live Training By Industry Experts
- 6 months of Research Project Experience Certificate
- 320+ hours of Live Training Interactive Sessions by Data Scientists
- Scholarships up to $4000 on the type of Research Project undertaken
- Placement Opportunities with our partners after course completion
- 100% Placement Assistance
Apply Now
Frequently Asked Questions
- Chatbots.
- Facial recognition.
- Image tagging.
- Natural language processing.
- Sales prediction.
- Self-driving cars.
- Sentiment analysis.
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.
We have a 24x7 LMS access for all our live online classes.
Students have the leverage to buy courses using credit card EMI.
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.