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ABOUT THE COURSE
This course helps you to understand the vectors in different dimensions and their linear transformation to some other domain, matrix theory and various properties of matrices and its transformations, Eigenvalues and Eigenvectors and their role in dimensionality reduction and the applications of linear algebra for AI and ML.COURSE OBJECTIVES
Upon successful completion of the course, the learner will be able to :In this topic, you will learn about the multidimensional vector space and its various representations. You will also understand the transformations between different domains.
In this topic, you will learn about Subspaces, span and Basis of a vector and how it reduces the complexity of representing the vectors.
In this topic, you will learn about Grouping multiple vectors to form matrices and various types of matrices and its properties.
In this topic, you will learn about how to derive Eigenvalues and Eigenvectors from the matrices and how they are used to reduce dimension.
In this topic, you will learn about how to find the inner product of the matrix and its properties and short cuts to find it. You will also learn how Linear Algebra is used in the field of AI and ML.
ABOUT THE COURSE
In this course, you will learn about probability, different types of distribution and density functions, joint distributions and density functions and the different moments, parameter estimation and minimum risk estimation and the application of probability in the field of Data Science and ML.COURSE OBJECTIVES
Upon successful completion of the course, the learner will be able to :In this topic, you will learn about introduction to probability and the various terms associated with it. You will understand conditional probability by solving various problems in random variables and finding probabilities for random processes.
In this topic, you will learn about different probability distribution and density functions. You will learn about choosing and applying appropriate Probability Densities functions.
In this topic, you will learn about joint distributions and Densities.You will also understand the various order of Moments.
In this topic, you will learn about choosing the correct parameter through parameter estimation and risk estimation for a given process.
In this topic, you will learn about the application of Probability in the field of Data Science and ML.
ABOUT THE COURSE
In this course, you will learn about Bayes Theorem and decision making using Bayes Theorem, designing and customizing the Bayesian theorem to work with more than 2 features and cost estimation for errors.COURSE OBJECTIVES
Upon successful completion of the course, the learner will be able to :In this topic, you will learn about how conditional probability is used to derive Bayes Theorem and decision making using Bayes Theorem.
In this topic, you will learn about designing and customizing the Bayesian theorem to work with more than 2 features.
In this topic, you will learn about how to create decision region classification by deriving Decision Boundaries and multidimensional Decision Boundaries.
In this topic, you will learn about how to calculate the cost of error and how to do an estimation of errors.
In this topic, you will learn about the application of Statistical Decision Making in the field of AI and ML.
ABOUT THE COURSE
In this course, you will learn about the basics of calculus, multivariable calculus and how to apply calculus on complex function using chain rule and optimization and regression using Calculus.COURSE OBJECTIVES
Upon successful completion of the course, the learner will be able to :In this topic, you will learn about Introduction to calculus and Introduction to building framework for modeling systems in which there is a change.
In this topic, you will learn about performing integration and differentiation with respect to multiple variables.
In this topic, you will learn about constructing complicated functions by substitution and differentiating such complicated functions using the chain rule.
In this topic, you will learn about how to identify the quantity to optimize in an ML algorithm using Calculus.
In this topic, you will learn about how to build regression models using Calculus.
ABOUT THE COURSE
In this course, you will learn about different pattern recognition systems, data preprocessing, dimensionality reduction and component analysis and discriminants.COURSE OBJECTIVES
Upon successful completion of the course, the learner will be able to :In this topic, you will learn about Different Pattern Recognition Systems and Different steps to follow Pattern Recognition, like, Sensing, Segmentation, Grouping, Feature Extraction, Classification and Post Processing.
In this topic, you will learn about the design cycle in Pattern Recognition and the different states in the design cycle like, Data Collection, Feature Choice, Model Choice, Training, Evaluation and Complexity analysis.
In this topic, you will learn about the different data processing techniques, which involve, Descriptive Data Summarization, Data Cleaning, Data Integration and Transformation, and Data Reduction.
In this topic, you will learn about the various problems that arises due to high dimensional data, with respect to Accuracy and Complexity and how an increase in the number of features lead to Overfitting of the Component.
In this topic, you will learn about the various Component Analysis like, PCA and the various Discriminant Analysis line, LDA and MDA.
ABOUT THE COURSE
In this course, you will learn about various branches in Machine learning, Artificial Neural Networks, classification and clustering and Deep Learning.COURSE OBJECTIVES
Upon successful completion of the course, the learner will be able to :In this topic, you will learn about finding the similarity between two features using distance metrics.
In this topic, you will learn about discovering interesting relations between variables in large databases using Association Rule Learning.
In this topic, you will learn about decision making using a decision support tool that uses a tree-like model of decisions.
In this topic, you will learn about replicating the way that the humans learn using Artificial Neural Networks.
In this topic, you will learn about performing classification by finding the hyperplane that differentiate the two classes using Support Vector Machines.
In this topic, you will learn about building probabilistic directed acyclic graphical model(network) to compute probability and classify.
In this topic, you will learn about various clustering algorithms that group set of objects in such a way that objects in the same group are more similar to each other.
In this topic, you will learn about systems that automatically discover the representations needed for feature detection or classification from raw data through Representation Learning and automatically performing some actions and seeing the results to learn how to behave in a environment using Reinforcement Learning.
In this topic, you will learn about constructing a network of neurons, training them by adjusting their weights using Back Propagation.
In this topic, you will learn about classifying images and perform object recognition using Deep Convolution Network.
In this topic, you will learn about combining feedforward neural networks with temporal-difference learning to train a program to learn to play certain board games like backgammon
ABOUT THE COURSE
In this course, you will learn about classification and clustering, analyzing data for patterns, or co-occurrence using Association Rules, trend analysis using Time series analysis and sequence discovery and deriving information through summarization techniques.COURSE OBJECTIVES
Upon successful completion of the course, the learner will be able to :In this topic, you will learn about various classification algorithms and their implementations.
In this topic, you will learn about various clustering algorithms that group set of objects in such a way that objects in the same group are more similar to each other.
In this topic, you will learn about analyzing data for patterns, or co-occurrence, in a database using Association Rule.
In this topic, you will learn about forming regression line for the given data and Predicting the outcome for a given input using the previously formed regression relation.
In this topic, you will learn about how to perform trend analysis using a statistical technique called Time Series Analysis.
In this topic, you will learn about finding statistically relevant patterns between data examples using Sequence Discovery.
In this topic, you will learn about deriving information from the data using various summarization techniques.
ABOUT THE COURSE
In this course, you will learn about Knowledge discovery theories-review, Data analysis on different domains and Spatial, Temporal, and Multimedia Databases and their analytics.COURSE OBJECTIVES
Upon successful completion of the course, the learner will be able to :In this topic, you will have a review on Knowledge discovery theories, models and systems, Deep learning and deep analytics, Scalable analysis and learning Knowledge discovery theories, models and systems.
In this topic, you will learn about Cloud computing and service data analysis, High performance computing for data analytics, VLDB Administration and Manageability, Tuning, Benchmarking and Performance Measurement.
In this topic, you will learn about Spatial, Temporal, and Multimedia Databases and Multimedia/stream/text/visual analytics.
ABOUT THE COURSE
In this course, you will learn about simulating human intelligence processes in machines, properly representing knowledge for reasoning and NLP, Social Intelligence, motion and manipulation according to situations.COURSE OBJECTIVES
Upon successful completion of the course, the learner will be able to :In this topic, you will learn about introduction to how to simulate human intelligence processes in machines.
In this topic, you will learn about Problem solving in AI that encompasses a number of techniques known as algorithms, heuristics, root cause analysis, etc.
In this topic, you will learn about incorporating the findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets.
In this topic, you will learn about representing knowledge in order to design formalisms that will make complex systems easier to design and build.
In this topic, you will learn about performing decision making tasks to achieve a specific goal by planning.
In this topic, you will learn about modelling and processing uncertain knowledge about an environment and correspondingly acting on it.
In this topic, you will learn about learning agents that learn something and try something different and learning from the previous experiences.
In this topic, you will learn about how to model neurons that communicate with each other, perceive knowledge and act as per the previous experience.
In this topic, you will learn about programming computers to process and analyze human (natural) languages through Natural Language Processing.
In this topic, you will learn about programming machines(robots) that can move and manipulate(eg., car assembling) and programming social intelligence to machine to coordinate with the society.
ABOUT THE COURSE
Artificial Intelligence is the development of computer systems to be able to perform tasks normally requiring human intelligence and Machine learning is an application that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. In this course you will learn how to apply these topic in real time, by applying it in various case studies.COURSE OBJECTIVES
Upon successful completion of the course, the learner will be able to :In this topic, you will learn about Image analysis, which is the ability of computers to recognize attributes within an image and implement the given case study step by step.
In this topic, you will learn about Video content analysis, which is the capability of automatically analyzing video to detect and determine temporal and spatial events and implement the given case study step by step.
In this topic, you will learn about Text analytics, which is the process of deriving high-quality information from text and implement the given case study step by step.
In this topic, you will learn about Artificial Intelligence and Big Data and implement the given case study step by step.
In this topic, you will learn about Forecasting, which is the process of making predictions of the future based on past and present data and most commonly by analysis of trends and implement the given case study step by step.
In this topic, you will learn about enhancing the Customer Experience using Machine Learning and implement the given case study step by step.
In this topic, you will learn about applying Artificial Intelligence in Supply Chain and implement the given case study step by step.
In this topic, you will learn about Behavioral analytics, which is an area of data analytics that focuses on providing insight into the actions of people and implement the given case study step by step.
In this topic, you will learn about applying Artificial Intelligence in Health Care and implement the given case study step by step.
In this topic, you will learn about applying Artificial Intelligence in Personality Analytics and implement the given case study step by step.
In this topic, you will learn about applying Artificial Intelligence in Consumer Packeged goods like food, beverages, cosmetics and cleaning products and implement the given case study step by step.
In this topic, you will learn about applying Artificial Intelligence in Consumer Packeged goods like food, beverages, cosmetics and cleaning products and implement the given case study step by step.
In this topic, you will learn about developing predictive models in Healthcare & Life Sciences and implement the given case study step by step.
In this topic, you will learn about applying Machine Learning in Insurance Market analysis and implement the given case study step by step.
"In this topic, you will learn about applying Machine Learning in Technology, Media, Telecom implement the given case study step by step."
In this topic, you will learn about applying Machine Learning in Retail analysis to forecast and make simple assumptions about customers and implement the given case study step by step.
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domains apart from the basic LMS components like
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