Final Exam: ML Programmer

Machine Learning
  • 1 Video | 30m 32s
  • Includes Assessment
  • Earns a Badge
Final Exam: ML Programmer will test your knowledge and application of the topics presented throughout the ML Programmer track of the Skillsoft Aspire ML Programmer to ML Architect Journey.

WHAT YOU WILL LEARN

  • apply label encoding on the features and target in your dataset and recognize its limitations when applied on input features use the Pandas library to one-hot encode one or more features of your dataset and distinguish between this technique and label encoding
    apply the MinMaxScaler on a dataset to get two similar columns to have the same range of values
    build, train and validate the Keras model by defining various components including the activation functions, optimizers and the loss function
    compare the differences between the ANOVA and ANCOVA approaches of statistical test
    configure, train and evaluate the linear regression model which makes predictions from multiple input features
    create a linear regression model using scikit-learn to predict the sale price of a house and evaluate this model using metrics such as mean squared error and r-square
    create and save machine learning models using scikit-learn
    create machine learning models in production set up machine learning models in production using Flask
    create training and validation sets for your regression model
    define Gradient descent and the different types of Gradient descent
    define NLP and the uses, benefits and challenges associated with NLP
    define recursive and recursively enumerable languages and their essential properties
    define regular expressions and list the theorems that are used to manage the semantics of regular expressions
    define the architecture for a Keras sequential model and initialize it
    define the architecture for a Keras sequential model and set the training parameters such as loss function and optimizer
    define the concept of Bayes theorem and its implementation in machine learning
    define the concept of Bayes theorem and its implementation in machine learning recall the essential ingredients of Bayesian statistics including prior distribution, likelihood function and posterior inference
    define the concept of Ensemble techniques and illustrate how Bagging and Boosting algorithms are used to manage predictions
    define the concept of vector norms and the different types of vector norms recognize various essential operations that we can perform on matrix (Matrix norms and Matrix identities) recognize how the Trace, Determinant, Inverse and Transpose operations are applied on Matrix
    define the technique of gradient descent optimization in order to find the optimal parameters for a neural network
    demonstrate how to analyze and process texts using Spacy
    demonstrate how to implement vector scalar multiplication using Python
    demonstrate stemming and lemmatization scenarios in NLP using NLTK
    demonstrate the approach of filtering stopwords in a tokenized sentence using NLTK
    demonstrate the steps involved in extracting topics using LDA
    demonstrate various tokenization use cases with NLTK
    describe hyperparameter and the different types of hyperparameter tuning methods
    describe hyperparameter and the different types of hyperparameter tuning methods demonstrate how to tune hyperparameters using grid search
    describe the approaches and steps involved in developing machine learning models
    describe the concept of Bayesian probability and statistical inference
  • describe the concept of Bayes theorem and its implementation in machine learning recall the essential ingredients of Bayesian statistics including prior distribution, likelihood function and posterior inference
    describe the concept of Bias, Variance and Regularization and their usages in evaluating Predictive models
    describe the concept of probability models and illustrate the use of Bayesian methods for problems with missing data
    describe the configurations required to use a neuron for linear regression
    describe the qualities of a logistic regression S-curve and understand the kind of data it can model
    describe Turing machines and their capabilities
    describe Turing machines and their capabilities list the prominent variations of themes that can be used to build Turing machines
    identify the role of probability and statistics in Bayesian analysis from the perspective of Frequentist and Subjective probability paradigm
    illustrate the concept and characteristics of central limit theorem and means with their prominent usage examples
    implement Bagging algorithms with the approach of Random Forest using Scikit-learn
    implement Markov chain simulation using Python
    implement Q-learning using Python
    list machine learning metrics that can be used to evaluate machine learning algorithms
    recognize how computational complexities can impact Turing machine models and language families
    recognize the different types of reinforcement learning that can be implemented for decision-making
    recognize the essential characteristics of probability that are applicable in Machine learning
    recognize the essential principles driving formal language and automata theory
    recognize the key differences between the reinforcement learning and machine learning paradigms
    recognize the key differences between the reinforcement learning and machine learning paradigms
    recognize the specific relationship which needs to exist between the input and output of a regression model
    reconstruct rectangular matrix from single-value decomposition
    represent the values in a column as a proportion of the maximum absolute value by using the MaxAbsScaler
    understand basis and projection of vectors in Python
    understand how to apply gaussian elimination in Python
    Understand how to work with linear transformations in Python
    use PyMC to define a model and arbitrary deterministic function and use the model to generate posterior samples
    use Python libraries to implement principal component analysis with matrix multiplication
    use the estimator's methods to train and evaluate the model and visualize its performance using Matplotlib
    use the Pandas library to load a dataset in the form of a CSV file and perform some exploratory analysis on its features
    use training and validation sets for your regression model

IN THIS COURSE

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