Overview
Cognixia’s Machine Learning, Artificial Intelligence and Deep Learning training course covers the latest machine learning algorithms while also talking about 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, going beyond the theoretical concepts of the technology like regression, clustering and classification, and discussing their applications as well.
Why this
course?
This course will help you learn:
- Overview of Machine Learning, Artificial Intelligence, and Deep learning
- Supervised and unsupervised learning concepts and modeling
- Solving business problems using Artificial Intelligence and Machine Learning
- Various theoretical concepts and how they relate to the practical aspects of machine learning and AI
- Application of concepts such as regression, clustering, classification, dimensional reduction, and engine recommendations
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 is included in AI? (Robotics, Agents, and more)
- Installing and setting up – 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 & 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 and their use cases?
- What are recommendation engines and how do they work?
- Types of recommendation types
- User-based recommendation
- Item-based recommendation
- Difference: User-based and item-based recommendation
- Recommendation use-case
- Hands-on/ Lab exercises
- What is time series data?
- Time series variables
- Different components of time series data
- Visualizing the data to identify time series components
- Implementing ARIMA model for forecasting
- Exponential smoothing models
- Identifying different time series scenarios based on which different Exponential Smoothing models can be applied
- Implementing respective ETS models for forecasting
- Hands-on/ Lab exercises
- What is Deep Learning?
- Biological Neural Networks
- Understanding Artificial Neural Networks
- Building an Artificial Neural Network
- How does ANN work?
- Important terminologies of ANN
Who should attend
Cognixia’s Machine Learning, AI, and Deep Learning course is highly recommended for:
- Data scientists or a Machine Learning experts
- Software and Application Developers
- Business Analysts/Analytics professionals
- Recent graduates looking to build a career in Artificial Intelligence
- Technology enthusiasts with a sound understanding of Machine Learning
- Software Architects or Software Engineers who wish to gain expertise in Machine Learning algorithms
Prerequisites
A sound understanding of programming languages such as R and Python will be beneficial, however, not mandatory.
FAQs
Generative AI is a subset of AI that focuses on understanding patterns and structure in data and then using that to create more data like it.
Until now, machines couldn’t exhibit behavior that was indistinguishable from human responses. Generative AI has made that possible. In a world that operates on the principle of ‘Survival of the Fittest’, embracing Generative AI could be the edge you need to boost your productivity, improve operations, be more reliable and consistent, and do so much more.
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Yes, absolutely! The Machine Learning, AI, and Deep Learning online course is a virtual, instructor-led program so you can enroll and learn from anywhere.
This course is designed to be highly hands-on for all learners. Roughly 70% of this course would be hands-on practical training while 30% would be theoretical studies.
Yes! There will be a pre- and post-assessment for every module during this Machine Learning online training course.
Interested in this Course?
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.
