About this course
Gain essential skills in today’s digital age to store, method and analyse knowledge to tell business choices. The course details may change based on changing technologies and market dynamics for more details contact thru the website to get the updated information
Gain essential skills in today’s digital age to store, method and analyse knowledge to tell business choices. The course details may change based on changing technologies and market dynamics for more details contact thru the website to get the updated information
More about
Skillspeed by Sanjay Verma
In this course, a part of the large knowledge Micro / Masters program, you may develop your data of massive knowledge analytics and enhance your programming and mathematical skills. you may learn to use essential analytic tools like Apache Spark and R.
Topics lined during this course include:
cloud-based huge knowledge analysis;
predictive analytics, together with probabilistic and applied math models;
application of large-scale knowledge analysis;
analysis of downside area and knowledge wants.
By the tip of this course, you maybe able to approach large-scale knowledge science issues with ability and initiative.
What you will learn
How to develop algorithms for the applied math analysis of massive data;
Knowledge of massive knowledge applications;
How to use elementary principles utilized in prognostic analytics;
Evaluate and apply applicable principles, techniques and theories to large-scale knowledge of science issues.
In this course, a part of the large knowledge Micro / Masters program, you may develop your data of massive knowledge analytics and enhance your programming and mathematical skills. you may learn to use essential analytic tools like Apache Spark and R.
Topics lined during this course include:
cloud-based huge knowledge analysis;
predictive analytics, together with probabilistic and applied math models;
application of large-scale knowledge analysis;
analysis of downside area and knowledge wants.
By the tip of this course, you maybe able to approach large-scale knowledge science issues with ability and initiative.
What you will learn
How to develop algorithms for the applied math analysis of massive data;
Knowledge of massive knowledge applications;
How to use elementary principles utilized in prognostic analytics;
Evaluate and apply applicable principles, techniques and theories to large-scale knowledge of science issues.
Section 1: easy rectilinear regression
Fit a straightforward rectilinear regression between 2 variables in R; Interpret output from R; Use models to predict a response variable; Validate the assumptions of the model.
Section 2: Modelling knowledge
Adapt the straightforward rectilinear the regression model in R to upset multiple variables; Incorporate continuous and categorical variables in their models; choose the best-fitting model by inspecting the R output.
Section 3: several models
Manipulate nested knowledgeframes in R; Use R to use synchronic linear models to giant data frames by stratifying the data; Interpret the output of learner models.
Section 4: Classification
Adapt linear models to require into consideration once the response could be a categorical variable; Implement provision regression (LR) in R; Implement Generalised linear models (GLMs) in R; Implement Linear discriminant analysis (LDA) in R.
Section 5: Prediction victimisation models
Implement the principles of building a model to try and do prediction victimisation classification; Split knowledge into coaching and check sets, perform cross validation and model analysis metrics; Use model choice for explaining knowledge with models; Analyse the overfitting and bias-variance trade-off in prediction issues.
Section 6: obtaining larger
Set up and apply sparklyr; Use logical verbs in R by applying native sparklyr versions of the verbs.
Section 7: supervised machine learning with sparklyr
Apply sparklyr to machine learning regression and classification models; Use machine learning models for prediction; Illustrate however distributed computing techniques may be used for “bigger” issues.
Section 8: Deep learning
Use large amounts of knowledge to coach multi-layer networks for classification; perceive a number of the guiding principles behind coaching deep networks, together with the utilization of autoencoders, dropout, regularization, and early termination; Use sparkly and liquid to coach deep networks.
Section 9: Deep learning applications and scaling up
Understand a number of the ways that during which large amounts of untagged knowledge and part labeled knowledge, is employed to coach neural network models; Leverage existing trained networks for targeting new applications; Implement architectures for object classification and object detection and assess their effectiveness.
Section 10: transferral it all at once
Consolidate your understanding of relationships between the methodologies given during this course, their relative strengths, weaknesses and vary of relevancy of those ways.
Fit a straightforward rectilinear regression between 2 variables in R; Interpret output from R; Use models to predict a response variable; Validate the assumptions of the model.
Section 2: Modelling knowledge
Adapt the straightforward rectilinear the regression model in R to upset multiple variables; Incorporate continuous and categorical variables in their models; choose the best-fitting model by inspecting the R output.
Section 3: several models
Manipulate nested knowledgeframes in R; Use R to use synchronic linear models to giant data frames by stratifying the data; Interpret the output of learner models.
Section 4: Classification
Adapt linear models to require into consideration once the response could be a categorical variable; Implement provision regression (LR) in R; Implement Generalised linear models (GLMs) in R; Implement Linear discriminant analysis (LDA) in R.
Section 5: Prediction victimisation models
Implement the principles of building a model to try and do prediction victimisation classification; Split knowledge into coaching and check sets, perform cross validation and model analysis metrics; Use model choice for explaining knowledge with models; Analyse the overfitting and bias-variance trade-off in prediction issues.
Section 6: obtaining larger
Set up and apply sparklyr; Use logical verbs in R by applying native sparklyr versions of the verbs.
Section 7: supervised machine learning with sparklyr
Apply sparklyr to machine learning regression and classification models; Use machine learning models for prediction; Illustrate however distributed computing techniques may be used for “bigger” issues.
Section 8: Deep learning
Use large amounts of knowledge to coach multi-layer networks for classification; perceive a number of the guiding principles behind coaching deep networks, together with the utilization of autoencoders, dropout, regularization, and early termination; Use sparkly and liquid to coach deep networks.
Section 9: Deep learning applications and scaling up
Understand a number of the ways that during which large amounts of untagged knowledge and part labeled knowledge, is employed to coach neural network models; Leverage existing trained networks for targeting new applications; Implement architectures for object classification and object detection and assess their effectiveness.
Section 10: transferral it all at once
Consolidate your understanding of relationships between the methodologies given during this course, their relative strengths, weaknesses and vary of relevancy of those ways.
To know more on SkillSpeed contact the official company website founded by Sanjay Verma