Final Exam: ML Programmer

WHAT YOU WILL LEARN

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  Implement bagging algorithms with the approach of random forest using scikit-learn
  

  understand how to work with linear transformations in python
  

  recognize the specific relationship which needs to exist between the input and output of a regression model
  

  build, train and validate the keras model by defining various components including the activation functions, optimizers and the loss function
  

  demonstrate stemming and lemmatization scenarios in nlp using nltk
  

  use pymc to define a model and arbitrary deterministic function and use the model to generate posterior samples
  

  describe turing machines and their capabilities
  

  recognize the key differences between the reinforcement learning and machine learning paradigms
  

  recognize the essential principles driving formal language and automata theory
  

  define regular expressions and list the theorems that are used to manage the semantics of regular expressions
  

  use the estimator's methods to train and evaluate the model and visualize its performance using matplotlib
  

  define recursive and recursively enumerable languages and their essential properties
  

  use python libraries to implement principal component analysis with matrix multiplication
  

  use training and validation sets for your regression model
  

  use the pandas library to load a dataset in the form of a csv file and perform some exploratory analysis on its features
  

  create and save machine learning models using scikit-learn
  

  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
  

  compare the differences between the anova and ancova approaches of statistical test
  

  define the concept of ensemble techniques and illustrate how bagging and boosting algorithms are used to manage predictions
  

  implement q-learning using python
  

  configure, train and evaluate the linear regression model which makes predictions from multiple input features
  

  describe turing machines and their capabilities list the prominent variations of themes that can be used to build turing machines
  

  represent the values in a column as a proportion of the maximum absolute value by using the maxabsscaler
  

  describe the qualities of a logistic regression s-curve and understand the kind of data it can model
  

  define the architecture for a keras sequential model and set the training parameters such as loss function and optimizer
  

  recognize the essential characteristics of probability that are applicable in machine learning
  

  demonstrate the approach of filtering stopwords in a tokenized sentence using nltk
  

  recognize the key differences between the reinforcement learning and machine learning paradigms
  

  recognize how computational complexities can impact turing machine models and language families
  

  illustrate the concept and characteristics of central limit theorem and means with their prominent usage examples
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  define the architecture for a keras sequential model and initialize it
  

  demonstrate how to analyze and process texts using spacy
  

  create training and validation sets for your regression model
  

  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 hyperparameter and the different types of hyperparameter tuning methods demonstrate how to tune hyperparameters using grid search
  

  define gradient descent and the different types of gradient descent
  

  describe the concept of bayesian probability and statistical inference
  

  describe the approaches and steps involved in developing machine learning models
  

  apply the minmaxscaler on a dataset to get two similar columns to have the same range of values
  

  identify the role of probability and statistics in bayesian analysis from the perspective of frequentist and subjective probability paradigm
  

  define the technique of gradient descent optimization in order to find the optimal parameters for a neural network
  

  recognize the different types of reinforcement learning that can be implemented for decision-making
  

  describe the concept of probability models and illustrate the use of bayesian methods for problems with missing data
  

  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
  

  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
  

  reconstruct rectangular matrix from single-value decomposition
  

  demonstrate the steps involved in extracting topics using lda
  

  define nlp and the uses, benefits and challenges associated with nlp
  

  define the concept of bayes theorem and its implementation in machine learning
  

  create machine learning models in production set up machine learning models in production using flask
  

  list machine learning metrics that can be used to evaluate machine learning algorithms
  

  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
  

  understand how to apply gaussian elimination in python
  

  demonstrate various tokenization use cases with nltk
  

  describe the concept of bias, variance and regularization and their usages in evaluating predictive models
  

  describe the configurations required to use a neuron for linear regression
  

  implement markov chain simulation using python
  

  demonstrate how to implement vector scalar multiplication using python
  

  understand basis and projection of vectors in python
  

  describe hyperparameter and the different types of hyperparameter tuning methods