An Introductory Guide to Machine Learning

• Machine Learning Part-1
  • Machine Learning and its Types in Machine Learning
  • Deep Learning and Recommender Systems in Machine Learning
  • What is Regression in Machine Learning
  • Univariate Linear Regression in Machine Learning
  • Multivariate Linear Regression in Machine Learning
  • Feature Scaling in Machine Learning
  • Linear Regression in Scikit Learn in Machine Learning
  • Regularization (Lasso, Ridge, and ElasticNet Regression) in Machine Learning
  • Support Vector Regression in Machine Learning
  • Nearest Neighbour Regression in Machine Learning
  • Decision Tree Regression in Machine Learning
  • Feature Engineering and Categorical Variables Encoding in Machine Learning
  • Numerical Variables Transformation in Machine Learning
  • Feature Selection (Filter Methods) in Machine Learning
  • Feature Selection (Wrapper Methods) in Machine Learning
  • Feature Selection (Intrinsic Methods) in Machine Learning
  • Model Evaluation Measures (Explained Variance Score, MAE, MSE) in Machine Learning
  • Model Evaluation Measures (Median Absolute Error, R^2 Score) in Machine Learning
  • Dummy Regressors in Machine Learning
  • Cross Validation in Machine Learning
  • Case Study: House Prices Prediction using Advanced Regression in Machine Learning
• Machine Learning Part-2
  • Types of Classification Problems in Machine Learning
  • Logistic Regression in Machine Learning
  • Support Vector Machines in Machine Learning
  • Decision Trees in Machine Learning
  • Naive bayes Part-1 in Machine Learning
  • Naive bayes Part-2 in Machine Learning
  • K-Nearest Neighbors in Machine Learning
  • Ensemble Learning Part 1 in Machine Learning
  • Ensemble Learning Part 2 in Machine Learning
  • XGBoost, Light GBM and CatBoost in Machine Learning
  • Learning Curves in Machine Learning
  • Model Evaluation Part 1 in Machine Learning
  • Model Evaluation Part 2 in Machine Learning
  • Dummy Estimators and Handling Imbalance Class Problem in Machine Learning
  • Hyper-Parameter Optimization and Kaggle Competition in Machine Learning
• Machine part-3
  • Unsupervised Learning in Machine Learning
  • K-Means Clustering in Machine Learning
  • Hierarchical Clustering in Machine Learning
  • DBSCAN Clustering and Customer Segmentation in Machine Learning
  • Apriori Algorithm and Association Rules in Machine Learning
  • Principal Component Analysis for Dimensionality Reduction in Machine Learning
  • Semi-Supervised Learning Techniques in Machine Learning
• Machine Learning Tools and Libraries in Machine Learning
  • Automated Machine Learning in Machine Learning
  • Pandas Profiling and PyCaret in Machine Learning
  • RAPIDS (Using GPU for Fast Computations) in Machine Learning
• Big Data Tools and Technologies in Machine Learning
  • What is Big Data ? in Machine Learning
  • Hadoop Ecosystem in Machine Learning
  • Map Reduce Framework in Machine Learning
  • Apache Spark and it's Components in Machine Learning