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Introduction to Machine learning

  • What is ML?
  • Types of ML
  • ML package :scikit-learn
  • Anaconda
  • How to install anaconda

Basic Introduction numpy and Pandas

  • Introduction to NumPy
  • Creating an array
  • Class and Attributes of ndarray
  • Basic Operations
  • Activity-Slice
  • Stack operations
  • Mathematical Functions of NumPy
  • Introduction to Pandas
  • Understanding DataFrame
  • Series
  • Concatenating and appending DataFrames
  • loc and iloc
  • Drop columns or rows
  • Groupby
  • Map and apply

Data Preprocessing

  • Introduction
  • Dealing with missing data
  • Handling categorical data
    • Encoding class labels
    • One-hot-encoding
  • Split data into training and testing sets
  • Bringing Features onto same scale


  • Introduction
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Evaluate Performance of a linear regression model
  • Overfitting and underfitting

K-Nearest Neighbors(KNN)

  • KNN theory
  • Implementing KNN with scikit-learn
  • KNN Parameters
    • n_neighbors
    • metric
  • How to find Nearest Neighbors
  • Writing Own KNN classifier from scratch

Logistic Regression

  • Logistic Regression theory
  • Implementing Logistic regression with scikit-learn
  • Logistic Regression Parameters
  • Multi-class classification
  • MNIST digit dataset with Logistic Regression
  • Predictive modeling on adult income dataset

Support Vector Machine(SVM)

  • SVM theory
  • Implementing SVM with scikit-learn
  • SVM Parameters:
    • C and gamma
  • Plot hyperplane for linear classification
  • Decision function

Decision Tree and Random Forest

  • Theory behind decision tree
  • Implementing decision tree with scikit-learn
  • Decision tree parameters
  • Combining multiple decision trees via Random forest
  • How random forest works..?

Naīve Bayes Classification

  • Theory Naive Bayes Algorithm
  • Features extraction
    • Countvectorizer
    • TF-IDF
  • Text Classification

Model Evaluation and Parameter Tuning

  • Cross validation via K-Fold
  • Tuning hyperparameters via grid search
  • Confusion matrix
  • Recall and Precision
  • ROC and AUC

Clustering and Dimension Reduction

  • K-means Clustering
  • Elbow method
  • Principal components analysis(PCA)
  • PCA step by step
  • Implementing PCA with scikit-learn
  • LDA with scikit-learn