MACHINE LEARNING USING PYTHON

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Machine Learning in AI
What you will get when you take this course:

Machine Learning (ML) Syllabus

MODULE 1: PYTHON FOR MACHINE LEARNING

  • Advantages of Python
  • Python compiler and PVM
  • Python instillation and environment
  • strings
  • char
  • lists
  • tuples
  • range
  • sets
  • dictionaries
  • if statement
  • if…else statement
  • if…elif…else statement
  • while loop
  • for loop
  • break statement
  • continue statement
  • pass statement
  • Array creation
  • Array attributes
  • 1D and 2D Arrays
  • Matrix
  • Built in and User defined functions
  • Writing your own functions
  • Importing functions
  • Modules
  • Packages
  • Imports
  • Series
  • Dataframes
  • Creation of dataframes from different sources
  • Viewing data in dataframe
  • Operations on dataframe
  • Handling missing data
  • Line plot
  • Bar graph
  • Pie chart
  • Subplots
  • Histogram
  • Distribution plot
  • Kde plot
  • Count plot
  • Box plot
  • Scatter plot
  • Sub plots
  • Lmplot
  • Pair plots

MODULE 2: MACHINE LEARNING IN AI

  • One hot encoding using dummy variables
  • One hot encoding using One hot encoder
  • Simple Linear regression
  • Multiple Linear regression
  • Polynomial Linear regression
  • Ridge regression
  • Bias and Variance tradeoff
  • Lasso regression
  • Elasticnet regression
  • Logistic regression
  • Naive Bayes (Gaussian NB and Multinomial NB)
  • KNN Classifier
  • SVM
  • Regularization
  • Kernel Trick
  • Decision Tree
  • Entropy
  • Gini Index
  • Random Forest
  • Conusion Matrix
  • Bootstrapping, Bagging and Boosting
  • K-Means Clustering
  • Elbow technique
  • Apriori Algorithm
  • Selecting appropriate model for our data

MODULE 3: DEEP LEARNING IN AI

  • Biological Neural Network
  • Artificial Neural Network
  • Perceptrons
  • Layers of a Network
  • Identity Function
  • Binary step function or Threshold function
  • Logistic function or Sigmoid function
  • ReLU function
  • Hyperbolic Tangent function
  • Softmax function
  • ANN
  • ANN with Activation functions
  • Variables
  • Constants
  • Placeholders
  • Graph / Tensor / Session

MODULE 4: NATURAL LANGUAGE PROCESSING IN AI (NLP)

  • Tokenization
  • Stemming
  • Lemmatization
  • Stop words
  • POS
  • CountVectorizer
  • Tf-idf Vectorizer

MODULE 5: COMPUTER VISION IN AI