Final Exam: ML Programmer
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 encodingapply the MinMaxScaler on a dataset to get two similar columns to have the same range of valuesbuild, train and validate the Keras model by defining various components including the activation functions, optimizers and the loss functioncompare the differences between the ANOVA and ANCOVA approaches of statistical testconfigure, train and evaluate the linear regression model which makes predictions from multiple input featurescreate 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-squarecreate and save machine learning models using scikit-learncreate machine learning models in production set up machine learning models in production using Flaskcreate training and validation sets for your regression modeldefine Gradient descent and the different types of Gradient descentdefine NLP and the uses, benefits and challenges associated with NLPdefine recursive and recursively enumerable languages and their essential propertiesdefine regular expressions and list the theorems that are used to manage the semantics of regular expressionsdefine the architecture for a Keras sequential model and initialize itdefine the architecture for a Keras sequential model and set the training parameters such as loss function and optimizerdefine the concept of Bayes theorem and its implementation in machine learningdefine 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 inferencedefine the concept of Ensemble techniques and illustrate how Bagging and Boosting algorithms are used to manage predictionsdefine 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 Matrixdefine the technique of gradient descent optimization in order to find the optimal parameters for a neural networkdemonstrate how to analyze and process texts using Spacydemonstrate how to implement vector scalar multiplication using Pythondemonstrate stemming and lemmatization scenarios in NLP using NLTKdemonstrate the approach of filtering stopwords in a tokenized sentence using NLTKdemonstrate the steps involved in extracting topics using LDAdemonstrate various tokenization use cases with NLTKdescribe hyperparameter and the different types of hyperparameter tuning methodsdescribe hyperparameter and the different types of hyperparameter tuning methods demonstrate how to tune hyperparameters using grid searchdescribe the approaches and steps involved in developing machine learning modelsdescribe 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 inferencedescribe the concept of Bias, Variance and Regularization and their usages in evaluating Predictive modelsdescribe the concept of probability models and illustrate the use of Bayesian methods for problems with missing datadescribe the configurations required to use a neuron for linear regressiondescribe the qualities of a logistic regression S-curve and understand the kind of data it can modeldescribe Turing machines and their capabilitiesdescribe Turing machines and their capabilities list the prominent variations of themes that can be used to build Turing machinesidentify the role of probability and statistics in Bayesian analysis from the perspective of Frequentist and Subjective probability paradigmillustrate the concept and characteristics of central limit theorem and means with their prominent usage examplesimplement Bagging algorithms with the approach of Random Forest using Scikit-learnimplement Markov chain simulation using Pythonimplement Q-learning using Pythonlist machine learning metrics that can be used to evaluate machine learning algorithmsrecognize how computational complexities can impact Turing machine models and language familiesrecognize the different types of reinforcement learning that can be implemented for decision-makingrecognize the essential characteristics of probability that are applicable in Machine learningrecognize the essential principles driving formal language and automata theoryrecognize the key differences between the reinforcement learning and machine learning paradigmsrecognize the key differences between the reinforcement learning and machine learning paradigmsrecognize the specific relationship which needs to exist between the input and output of a regression modelreconstruct rectangular matrix from single-value decompositionrepresent the values in a column as a proportion of the maximum absolute value by using the MaxAbsScalerunderstand basis and projection of vectors in Pythonunderstand how to apply gaussian elimination in PythonUnderstand how to work with linear transformations in Pythonuse PyMC to define a model and arbitrary deterministic function and use the model to generate posterior samplesuse Python libraries to implement principal component analysis with matrix multiplicationuse the estimator's methods to train and evaluate the model and visualize its performance using Matplotlibuse the Pandas library to load a dataset in the form of a CSV file and perform some exploratory analysis on its featuresuse training and validation sets for your regression model
IN THIS COURSE
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