Final Exam: ML Programmer
Machine Learning | Beginner
- 1 video | 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
Implement bagging algorithms with the approach of random forest using scikit-learnunderstand how to work with linear transformations in pythonrecognize the specific relationship which needs to exist between the input and output of a regression modelbuild, train and validate the keras model by defining various components including the activation functions, optimizers and the loss functiondemonstrate stemming and lemmatization scenarios in nlp using nltkuse pymc to define a model and arbitrary deterministic function and use the model to generate posterior samplesdescribe turing machines and their capabilitiesrecognize the key differences between the reinforcement learning and machine learning paradigmsrecognize the essential principles driving formal language and automata theorydefine regular expressions and list the theorems that are used to manage the semantics of regular expressionsuse the estimator's methods to train and evaluate the model and visualize its performance using matplotlibdefine recursive and recursively enumerable languages and their essential propertiesuse python libraries to implement principal component analysis with matrix multiplicationuse training and validation sets for your regression modeluse the pandas library to load a dataset in the form of a csv file and perform some exploratory analysis on its featurescreate and save machine learning models using scikit-learndefine 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 inferencecompare the differences between the anova and ancova approaches of statistical testdefine the concept of ensemble techniques and illustrate how bagging and boosting algorithms are used to manage predictionsimplement q-learning using pythonconfigure, train and evaluate the linear regression model which makes predictions from multiple input featuresdescribe turing machines and their capabilities list the prominent variations of themes that can be used to build turing machinesrepresent the values in a column as a proportion of the maximum absolute value by using the maxabsscalerdescribe the qualities of a logistic regression s-curve and understand the kind of data it can modeldefine the architecture for a keras sequential model and set the training parameters such as loss function and optimizerrecognize the essential characteristics of probability that are applicable in machine learningdemonstrate the approach of filtering stopwords in a tokenized sentence using nltkrecognize the key differences between the reinforcement learning and machine learning paradigmsrecognize how computational complexities can impact turing machine models and language familiesillustrate the concept and characteristics of central limit theorem and means with their prominent usage examples
define the architecture for a keras sequential model and initialize itdemonstrate how to analyze and process texts using spacycreate training and validation sets for your regression modeldescribe 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 inferencedescribe hyperparameter and the different types of hyperparameter tuning methods demonstrate how to tune hyperparameters using grid searchdefine gradient descent and the different types of gradient descentdescribe the concept of bayesian probability and statistical inferencedescribe the approaches and steps involved in developing machine learning modelsapply the minmaxscaler on a dataset to get two similar columns to have the same range of valuesidentify the role of probability and statistics in bayesian analysis from the perspective of frequentist and subjective probability paradigmdefine the technique of gradient descent optimization in order to find the optimal parameters for a neural networkrecognize the different types of reinforcement learning that can be implemented for decision-makingdescribe the concept of probability models and illustrate the use of bayesian methods for problems with missing datacreate 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-squaredefine 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 matrixreconstruct rectangular matrix from single-value decompositiondemonstrate the steps involved in extracting topics using ldadefine nlp and the uses, benefits and challenges associated with nlpdefine the concept of bayes theorem and its implementation in machine learningcreate machine learning models in production set up machine learning models in production using flasklist machine learning metrics that can be used to evaluate machine learning algorithmsapply 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 encodingunderstand how to apply gaussian elimination in pythondemonstrate various tokenization use cases with nltkdescribe the concept of bias, variance and regularization and their usages in evaluating predictive modelsdescribe the configurations required to use a neuron for linear regressionimplement markov chain simulation using pythondemonstrate how to implement vector scalar multiplication using pythonunderstand basis and projection of vectors in pythondescribe hyperparameter and the different types of hyperparameter tuning methods
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