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Learn AI - Introduction to Artificial Intelligence (AI) Course
Rated 4.5/5 based on 62 customer reviews

Learn AI - Introduction to Artificial Intelligence (AI) Course

Step into the future by mastering AI

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Modes of Delivery

Online Classroom

Collaborative, enriching virtual sessions, led by world class instructors at time slots to suit your convenience.

Classroom

Our classroom training provides you the opportunity to interact with instructors and benefit from face-to-face instruction.

Team/Corporate Training

Our Corporate training is carefully structured to help executives keep ahead of rapidly evolving business environments.
Group Discount: 10.00% for 2 people 15.00% for 3 to 4 people 20.00% for 5 and above people

3 Months FREE Access to all our E-learning courses when you buy any course with us

Curriculum

Module 1- Foundations of AI

Learning Objectives: Learn how to build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you. Develop expertise in popular AI & ML technologies and problem-solving methodologies. Develop the ability to independently solve business problems using Artificial Intelligence & Machine Learning

Topics

  • What is AI?
  • Python for AI
  • Probability & Statistics
  • Visualization Techniques
  • Case Study
Hands-on:

  • Write Python code to analyze, manipulate and visualize data
  • Learn to implement statistical techniques with Microsoft Excel
  • Write Python code using Python library - matplotlib, seaborn to visualize data and represent it graphically
  • Conduct exploratory data analysis in python to identify potential revenue maximisation opportunities and also visualize dat

Module 2- Machine Learning : Supervised Learning

Learning Objectives:Learn about supervised learning techniques - regression and classification. Understand techniques to build Decision Trees

Topics

  • Regression (Linear, Multiple and Logistic)
  • Classification (K-NN, Naive Bayes) Techniques
  • Decision Trees
  • Case Study

Hands-on: This dataset classifies people described by a set of attributes as good or bad credit risks. Using classification techniques, build a model to predict good or bad customers to help the bank decide on granting loans to its customers

Module 3- Machine Learning : Unsupervised Learning

Learning Objectives:  Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Understand Elbow method and Silhouette method

Topics

  • K-means Clustering
  • Hierarchical Clustering
  • High-dimensional Clustering
  • Case Study
Hands-on: In marketing, if you’re trying to talk to everybody, you’re not reaching anybody.. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns.

Module 4- Machine Learning : Ensemble Techniques

Learning Objectives:Learn about bootstrap sampling and its advantages followed by bagging.Boost model performance with Boosting. Learn Random Forest with real-life case study and how it helps avoid overfitting compared to decision trees

Topics

  • Boosting
  • Bagging
  • Random Forest
  • Case Study

Hands-on: In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. In this case study, use AdaBoost, GBM & Random Forest on Lending Data to predict loan status. Ensemble the output and see your result perform than a single model

Module 5- Machine Learning : Reinforcement Learning

Learning Objectives: Understand the basics of RL and its applications in AI. Markov Decision Processes: Model processes as Markov chains, learn algorithms for solving optimisation problems. Write Q-learning algorithms to solve complex RL problems.

Topics

  • Value based methods
  • Q-learning
  • Policy-based methods

Hands-on: No hands-on

Module 6- Deep Learning

Learning Objectives: Learn advanced machine learning techniques using the Neural Networks algorithms. Neural Networks can enable pattern recognition based on a large amount of inputs. Learn how NN algorithms work, and end up with an introduction to deep learning
Covers various activation functions like sigmoid, hyperbolic-tangent, Rectified Linear Units, Leaky Rectified Linear Units

Topics

  • Neural Network Basics
  • Deep Neural Networks
  • TensorFlow using Neural Networks & Deep Learning
  • Case Study

Hands-on: The research aimed at the case of customers’ default payments in Taiwan. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients.

Module 7- Natural Language Processing

Learning Objectives: Get started with the Natural language toolkit, learn the basics of text processing in python. Learn how to extract features from unstructured text and build machine learning models on text data. Conduct sentiment analysis, learn to parse English sentences and extract meaning from them. Explore the applications of text analytics in new areas and various business domains.

Topics

  • Statistical NLP and text similarity
  • Text Summarization
  • Syntax and Parsing techniques
  • Semantics and Generation
  • Case Study

Hands-on: Stock market prediction has been an interesting research topic for many years. Finding an efficient and effective means of studying the market perceptions found its way in different social networking platforms such as Twitter. With proper tools and the help of technology, meaningful and precious information can be gathered, analyzed, and utilized in different areas like in the movement and performance of the stock market.

Module 8- Computer Vision

Learning Objectives: Learn to use the power of computer vision and play with what you see, detect faces, eyes and other attributes using Haar cascades

Topics

  • Convolutional Neural Networks
  • Keras Library for Deep Learning in Python
  • Pre-processing Image Data
  • Object and face recognition using OpenCV
  • Case Study

Hands-on:  While we drive on a highway, we tend to feel sleepy. In this project, using OpenCV and implementing object detection and feature extraction we detect fatigue in real-time and report an alarm which will not only keep a driver attentive while driving but also reduce number of accidents.

Module 9- Intelligent Agents

Learning Objectives: Learn the AI search technique that employs heuristic for its moves. Understanding the fundamental concepts of genetic algorithms and visualize the evolution

Topics

  • Uniform and heuristic-based search techniques
  • Planning and constraint satisfaction techniques
  • Adversarial search and its uses
  • Case Study

Hands-on: Use cutting edge AI techniques to teach a computer to play a computer game

Projects

Covers projects using Logistic Regression, Decision Tree, K-Nearest Neighbor, Neural Networks, Adaboost, GBM, Random Forest, Building game playing agent, Object Detection and Tracking using OpenCV


Key Features

50 hours of Instructor led Training
Comprehensive Hands-on with Python
Covers machine learning algorithms such as Linear Regression, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Decision Trees, K-means clustering
Learn Deep Learning Techniques using TensorFlow and Keras
Learn to build a computer vision application

Our Students See All

Attended a 2 day weekend course by Knowledgehut for the CSM certification. The instructor was very knowledgeable and engaging. Excellent experience.

Attended workshop in April 2018

The CSPO Training was awesome and great. The trainer Anderson made all the concepts look so easy and simple. Using his past experience as examples to explain various scenarios was a plus. Moreover, it was an active session with a lot of participant involvement which not only made it interactive but interesting as well. Would definitely recommend this Training.

Attended workshop in July 2018

Great course. An interesting and interactive session to better understand how to succeed in formulating a business case and how to present it effectively.

Attended workshop in May 2018

The training was very interactive and engaging with the attendees.

Attended workshop in June 2018
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Jin Shi

Director at Timber creek Asset Management from Toronto, Canada
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Richard Dsouza

Business Analyst at Valtech from Bangalore, India
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Wily Salim

Services Project Engineer at Lendlease from Sydney, Australia
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Anish Maidh

Senior Project Manager at Telstra from Melbourne, Australia

Frequently Asked Questions

Artificial intelligence is the technology of making our systems more intelligent and providing solutions to problems. AI is the hottest career in this digital age and AI experts certainly earn the big bucks. According to Neuvoo, the average salary for Artificial Intelligence related jobs is $73,552 per year or $38 per hour. This is around 2.5 times more than the average salary in America. This course will help you understand the core concepts of AI and use it to build intelligent solutions. You will also get in-depth prep help to clear interviews and land jobs.

On completing this course you will:

  • Get advanced knowledge on machine learning techniques
  • Learn about Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks
  • Gain knowledge about how artificial intelligence can be implemented in real-time
  • Be proficient with computer vision tool: OpenCV

By the end of this course, you will gain

  • Strong knowledge on Machine Learning Techniques
  • Ability to build a game playing agent
  • Learn to build real-time object detectors

There are no restrictions but participants would benefit if they have sound knowledge in Python and familiarity with Data Science.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut

Your instructors are AI experts who have years of industry experience.

Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details: http://www.knowledgehut.com/refund

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our
Refund Policy

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

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