Complete-Applied-Machine-Learning-with-Projects-Series
This repository contains everything you need to become proficient in Applied Machine Learning
Youtube for all the implemented projects and tech interview resources - Ignito Youtube Channel
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
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 basics
Data Visualization Projects
Data Visualization using Plotly and Bokeh
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
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
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
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
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
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