Build Machine Learning Pipeline Using Scikit Learn

Reading list

Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable

Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables

Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN

Sample and Population Central Limit Theorem Confidence Interval and Margin of Error

Bivariate Analysis Introduction

Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables

Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables

Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables

Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL

Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model

Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification

Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2

Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building

Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python

Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation?Hold-Out Validation Understanding K Fold Cross Validation

Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R

Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree?Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees

Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools

Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings!Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes

Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification

Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?

Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt

Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R

Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis

Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models

Understand Basics of Recommendation Engine with Case Study

8 Ways to Improve Accuracy of Machine Learning Models

Introduction to Dask Working with CuML

Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values

Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML

Pickle and Joblib Introduction to Model Deployment

Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker

Reading list

Basics of Machine Learning

Machine Learning Lifecycle

Importance of Stats and EDA

Understanding Data

Probability

Exploring Continuous Variable

Exploring Categorical Variables

Missing Values and Outliers

Central Limit theorem

Bivariate Analysis Introduction

Continuous - Continuous Variables

Continuous Categorical

Categorical Categorical

Multivariate Analysis

Different tasks in Machine Learning

Build Your First Predictive Model

Evaluation Metrics

Preprocessing Data

Linear Models

KNN

Selecting the Right Model

Feature Selection Techniques

Decision Tree

Feature Engineering

Naive Bayes

Multiclass and Multilabel

Basics of Ensemble Techniques

Advance Ensemble Techniques

Hyperparameter Tuning

Support Vector Machine

Advance Dimensionality Reduction

Unsupervised Machine Learning Methods

Recommendation Engines

Improving ML models

Working with Large Datasets

Interpretability of Machine Learning Models

Automated Machine Learning

Model Deployment

Deploying ML Models