Classical machine Learning https://miro.medium.com/max/700/1*Z4suIxll8gd0KofJB4f1Cw.png Machine learning (ML) is a subset of artificial intelligence (AI) that allows software applications to perform a task without being explicitly programmed to do so. Machine Learning is the brain of Robots like Sophia. When it comes to machine learning, People jump directly to algorithms and implement them without knowing backend of the algorithms. Implementing ML model is not a big task, people have already written codes for you and within 5 lines of code you will be able to implement and evaluate any ml algorithm. But that’s not the goal. Here goal is to implement them right. For example: I have seen Data experts interpreting results of Linear regression without performing residual analysis and goodness of fit test. In order to start Machine Learning, first understand the terminologies around machine learning and its types. Then look at the algorithms for every types as given in the above image. I haven’t mentioned Semi-Supervised algorithms, but if interesting. Explore here: http://pages.cs.wisc.edu/~jerryzhu/pub/sslicml07.pdf Topics to be covered: • Machine Learning Types — Supervised, Semi-supervised and Unsupervised • Classification and Regression Problems • Bias-Variance Tradeoff • Underfitting and Overfitting Problems • Imbalanced Dataset and how to deal with it. • Model Evaluation Techniques for CLassification and Regression Supervised Algorithms: Classification • Naive Bayes • Logistic Regression • Decision Trees • K-Nearest Neighbors • Support Vector Machines • Bagging Trees — Random Forests • Boosted Trees — Adaboost, GBM, XGBoost and Light GBM Regression • Linear Regression, Lasso Regression and Ridge Regression • Decision Trees • K-Nearest Neighbors • Ensemble Techniques Unsupervised Techniques: Clustering — Partition based technique (K-means) and Hierarchical Clustering (Agglomerative and Divisive Clustering) Dimensionality Reduction Techniques — Principal Component Analysis, Factor Analysis and Singular Vector Decomposition Market Basket Analysis — Apriori Algorithm, FP Growth Technique. Time Series Analysis : Stationarity condition, Auto-Regressive model, Moving Average Model, ARIMA, ARIMAX, SARIMA. Recommendation System: Collaborative and Content Based filtering recommendation system Text Mining: Text Data Preparation techniques, Text Classification, Sentimental Analysis, Topic Modelling and Name entity recognition.