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Machine Learning, AI, & Deep Learning Training

Overview

Cognixia’s Machine Learning, Artificial Intelligence and Deep Learning training program discusses the latest machine learning algorithms while also covering the common threads that can be used in the future for learning a wide range of algorithms. The course is a complete package that will help learners build their skillsets and meet the demand of the ML-AI industry which is growing by leaps and bounds in recent years. This online course on Machine Learning, Deep Learning and Artificial Intelligence goes beyond the theoretical concepts of the technology like regression, clustering, classification, etc. and discusses their applications as well.

Certification

Participants will be awarded with an exclusive certificate upon successful completion of the program. Every learner is evaluated based on their attendance in the sessions, their scores in the course assessments, projects, etc. The certificate is recognized by organizations all over the world and lends huge credibility to your resume.

Schedule Classes

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What You'll learn

  • Introduction to Machine Learning, Artificial Intelligence, and Deep learning
  • Supervised and unsupervised learning concepts and modelling
  • Solving business problems using Artificial Intelligence and Machine Learning
  • Comprehending theoretical concepts and how they relate to the practical aspects of machine learning and AI
  • Applying concepts such as regression, clustering, classification, dimensional reduction and engine recommendation

Machine Learning, AI, and Deep Learning course is highly recommended for:

  • Developers aspiring to be data scientists or a Machine Learning experts
  • Business Analysts/Analytics professionals
  • Fresh graduates looking to build a career in Artificial Intelligence
  • Technology enthusiasts with a sound understanding of Machine Learning
  • Architects or Software Engineers who wish to gain expertise in Machine Learning algorithms

Duration: 60 Hours

Curriculum

  • What is Machine Learning?
  • Machine Learning use-cases
  • Machine Learning process flow
  • Machine Learning categories
  • What is AI?
  • Applications of AI
  • History of AI
  • Inductive Reasoning and Deductive Reasoning
  • What all is included in AI? (Robotics, Agent, and more)
  • Installation and setup – R and R Studio
  • Fundamentals: Vector, function, packages
  • Matrices: Building, naming dimensions, operations, visualizing, sub-setting
  • Data Frames: Building, merging, visualizing (ggplot2)
  • Hands-on/Lab exercises
  • Data analysis pipeline
  • What is Data Extraction?
  • Types of Data
  • Raw and processed data
  • Data wrangling
  • Exploratory data analysis
  • Visualization of data
  • Loading different types of datasets in R
  • Arranging the data
  • Plotting the graphs
  • Hands-on/Lab exercises

Supervised and unsupervised learning

  • Simple linear regression
  • Multiple linear regression
  • Support vector machine
  • Hands-on/ Lab exercises
  • Classification
  • What is a decision tree?
  • Algorithm for decision tree induction
  • Creating a perfect decision tree
  • Confusion matrix
  • What is a random forest?
  • What is a Navies Bayes?
  • Support vector machine: Classification
  • Hands-on/ Lab exercises
  • What is Clustering? (Including use-cases)
  • What is K-means Clustering?
  • What is C-means Clustering?
  • What is hierarchical Clustering?
  • Hands-on/ Lab exercises
  • Feature Extraction with PCA
  • Feature Selection techniques
  • What are Association Rules & their use cases?
  • What is Recommendation Engine & it’s working?
  • Types of Recommendation Types
  • User-Based Recommendation
  • Item-Based Recommendation
  • Difference: User-Based and Item-Based Recommendation
  • Recommendation Use-case
  • Hands-on/ Lab
  • What is Time Series data?
  • Time Series variables
  • Different components of Time Series data
  • Visualize the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • Implement respective ETS model for forecasting
  • Hands-on/ Lab
  • What is Deep Learning
  • Biological Neural Networks
  • Understand Artificial Neural Networks
  • Building an Artificial Neural Network
  • How ANN works
  • Important Terminologies of ANN

Prerequisites

A sound understanding of programming languages such as R and Python will be beneficial, however, not mandatory.

Reach out to us for more information

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Course features

Course Duration
Course Duration

36 hours of live, online, instructor-led training

24x7 Support
24x7 Support

Technical & query support round the clock

Lifetime LMS Access
Lifetime LMS Access

Access all the materials on LMS anytime, anywhere

Price Match Gurantee
Price match Gurantee

Guranteed best price aligning with quality of deliverables

FAQs

Certified Industry Experts/Subject Matter Experts with immense experience under their belt.

To attend the live virtual training, at least 2 Mbps of internet speed would be required.

You will have a lifetime access to our Learning Management System (LMS) which includes Class recordings, presentations, sample code and projects. You will be able to view the recorded sessions on it. We also have a technical support team to assist you in case you have any query.