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Investment Management with Python and Machine Learning Specialization Certification

Investment Management with Python and Machine Learning Specialization Certification

Get Investment Management with Python and Machine Learning Specialization Certificate from  MachineLearning.org.in which you can share in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.

Skills you must know

  • Risk Management
  • Portfolio construction and analysis
  • Python programming skills
  • Implementation of data science techniques in investment decisions
  • Portfolio Optimization
  • Programming skills
  • Managing your own personal invetsments
  • Investment management knowledge
  • Computer Science
  • Expertise in data science

Exam Details

  • Format: Multiple Choice Question
  • Questions: 10
  • Passing Score: 8/10 or 80%
  • Language: English

Here are the questions and answers :

Supervised learning deals with unlabeled data, while unsupervised learning deals with labelled data.

  • True
  • False

Which of the following groups are not Machine Learning techniques?

  • Regression
  • Classification
  • Scikit-Learn
  • Clustering

Which of the following is the LEAST accurate with regard to supervised learning?

  • Inputs and outputs are labeled to allow the ML algorithms to learn to map the inputs to their desired outputs
  • If no outputs are provided, the ML algorithms are trained to recognise patterns in the input data
  • The trained ML algorithms are able to make predictions or detect patterns when given new data without labels
  • None of the above

Which of the following is LEAST likely a symptom of underfitting in machine learning?

  • True parameters are treated as noise
  • The model fails to identify actual patterns in the data
  • Input and output data are learnt too exactly
  • All of the above

What is the most significant phase in a genetic algorithm?

  • Selection
  • Mutation
  • Crossover
  • Fitness function

Why K-fold CV fails in finance?

  • Observations cannot be assumed to be drawn from an IID process. However, information leakage will take place when the training set contains information that also appears in the testing set.
  • Testing set is used multiple times in the process of developing a model, leading to multiple testing and selection bias.
  • All of the above

Machine learning algorithms are used to detect fraud, automate trading activities, and provide financial advisory services to investors.

  • True
  • False

_ trading can simultaneously analyze large volumes of data and make thousands of trades every day

  • Algorithmic
  • Arithmetic
  • Advance
  • Robot

Algorithmic trading does make trading decisions based on emotions

  • True
  • False

_ are online applications that are built using machine learning, and they provide automated financial advice to investors.

  • Sharks
  • Robo-advisors
  • angel investors
  • All of the above

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