Complete-Applied-Machine-Learning-with-Projects-Series

This repository contains everything you need to become proficient in Applied Machine Learning

Complete Cheat Sheet for Tech Interviews - How to prepare efficiently

I took theses Projects Based Courses to Build Industry aligned Data Science and ML skills

Part 1 - How to solve Any ML System Design Problem

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Data Science using Python

Pandas

Numpy

Advanced Pandas Techniques

Data Pre-processing

Handling missing values

Data Cleaning

Mean/mode/median Imputation

Hot Deck Imputation

Rescale Data

Binarize Data

Regression Imputation

Stochastic regression imputation

Feature Scaling

Data Augmentation

Read and Process Large Datasets

Data Profiling

Summary Functions

Indexing

Grouping

Linear Regression

Multi Linear Regression

Polynomial Regression

Regression

Support Vector Regression

Decision Tree Regression

Random Forest Regression

Feature Engineering

GroupBy Features

Categorical and Numerical Features

Missing Value Analysis

Fill the missing Values

Unique Value Analysis

Univariate Analysis

Bivariate Analysis

Multivariate Analysis

Correlation Analysis

Spearman’s ρ

Pearson’s r

Kendall’s τ

Cramér’s V (φc)

Phik (φk)

Data Visualization

Data Visualization basics

Data Visualization Projects

Data Visualization using Plotly and Bokeh

Statistics

Random Variables

Statistical Inferences

Probability

Standard deviation and variance

Statistical Distributions

Hypothesis Testing

Normal distribution

t-distribution

Bernoulli distribution

Confidence intervals

Data Collection and Data Cleaning

Data Collection

Data Cleaning

Data Manipulation

Join

Melt

Cut

Transform

Clean

Slicing

Reshaping

Filter

Group by

Pivot and Merge

Concatenate

MultiIndexing

Stacking

Hierarchical indexing

Aggregate

Summarize data

Linear Algebra for Machine Learning

Linear algebra concepts in Python

Matrix operations

Advanced linear algebra procedures

Supervised Learning

Regression

Supervised learning with probabilistic models

linear regression

Ordinary Least Squares

Linear Models

Linear and Quadratic Discriminant Analysis

Support Vector Machines

Stochastic Gradient Descent

Nearest Neighbors

Gaussian Processes

Cross decomposition

Naive Bayes

Decision Trees

Ensemble methods

Feature selection

Ridge Regression

Bias-variance tradeoff

Regression analysis

Bayesian Methods

Lagrange multipliers tool

sparse regression model

estimate covariants

Bayesian linear regression

Classification Algorithms

Classification using nearest neighbors

K-nearest neighbors

Bayes classifier

Supervised learning classification

perceptron algorithm

Logistic Regression

Kernel Methods

Gaussian Processes

kernel

kernelized perceptron

Support Vector Machines and Decision Trees

Hyperplanes with maximum margin method

SVM

decision tree-based classifiers

Grid search hyperparameters

Boosting and K-Means Clustering

Bagging and boosting techniques

Characteristics of K-means tools

Label encoder

Unsupervised Learning

Clustering Methods

K-means

soft K-means

Gaussian mixture model

Principal Component Analysis and Markov Models

PCA basics

Implement PCA

Implement Markov chains using quantecon

Hidden Markov Models and Kalman Filtering

Hidden Markov Model

Markov models

Gaussian models

Forward/backward algorithm

Modeling

Model Training and Evaluation

Model Baselines

Model Tuning and Optimization

Model Review and governance

Automated Model retraining

Model Deployment and monitoring

Model Inference and Serving

Model Resource Management Techniques

Model Analysis

High-Performance Modeling

Model selection and evaluation

Cross-validation

Hyper-parameters Tuning

Performance Metrics

Validation curves

Applied Machine Learning Projects(40)

Applied Machine Learning projects repo