Kick-start your career in Artificial Intelligence and Machine Learning

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We learn what Unsupervised Learning algorithms are, working of the algorithms and their scope of application – Clustering and Dimensionality Reduction.

• • K-means clustering
    K-means clustering is a popular unsupervised learning algorithm to resolve the clustering problems in Machine Learning or Data Science. In this module, you will learn how the algorithm works and later implement it.
  • Hierarchical clustering
    Hierarchical Clustering is an ML technique or algorithm to build a hierarchy or tree-like structure of clusters. For example, it is used to combine a list of unlabeled datasets into a cluster in the hierarchical structure. This module will teach you the working and implementation of this algorithm.
  • High-dimensional clustering
    High-dimensional Clustering is the clustering of datasets by gathering thousands of dimensions.
  • Dimension Reduction-PCA
    Principal Component Analysis for Dimensional Reduction is a technique to reduce the complexity of a model like eliminating the number of input variables for a predictive model to avoid overfitting. Dimension Reduction-PCA is a well-known technique in Python for ML, and you will learn everything about this method in this module.

In this Machine Learning online course, we discuss supervised standalone models’ shortcomings and learn a few techniques, such as Ensemble techniques to overcome these shortcomings.

• • Decision Trees
    Decision Tree is a Supervised Machine Learning algorithm used for both classification and regression problems. It is a hierarchical structure where internal nodes indicate the dataset features, branches represent the decision rules, and each leaf node indicates the result.
  • Random Forests
    Random Forest is a popular supervised learning algorithm in machine learning. As the name indicates, it comprises several decision trees on the provided dataset’s several subsets. Then, it calculates the average for enhancing the dataset’s predictive accuracy.
  • Bagging
    ​​​​​​​Bagging, also known as Bootstrap Aggregation, is a meta-algorithm in machine learning used for enhancing the stability and accuracy of machine learning algorithms, which are used in statistical classification and regression.
  • Boosting
    As the name suggests, Boosting is a meta-algorithm in machine learning that converts robust classifiers from several weak classifiers. Boosting can be further classified as Gradient boosting and ADA boosting or Adaptive boosting.

Learn various concepts that will be useful in creating functional machine learning models like model selection and tuning, model performance measures, ways of regularisation, etc.

• • Feature engineering
    Feature engineering is transforming data from the raw state to a state where it becomes suitable for modelling. It converts the data columns into features that are better at representing a given situation in terms of clarity. Quality of the component in distinctly representing an entity impacts the model’s quality in predicting its behaviour. In this module, you will learn several steps involved in Feature Engineering.
  • Model selection and tuning
    This module will teach you which model best suits architecture by evaluating every individual model based on the requirements.
  • Model performance measures
    In this module, you will learn how to optimise your machine learning model’s performance using model evaluation metrics.
  • Regularising Linear models
    In this module, you will learn the technique to avoid overfitting and increase model interpretability.
  • ML pipeline
    This module will teach you how to automate machine learning workflows using the ML Pipeline. You can operate the ML Pipeline by enabling a series of data to be altered and linked together in a model, which can be tested and evaluated to achieve either a positive or negative result.
  • Bootstrap sampling
    Bootstrap Sampling is a machine learning technique to estimate statistics on population by examining a dataset with replacement.
  • Grid search CV
    Grid search CV is the process of performing hyperparameter tuning to determine the optimal values for any machine learning model. The performance of a model significantly depends on the importance of hyperparameters. Doing this process manually is a tedious task. Hence, we use GridSearchCV to automate the tuning of hyperparameters.
  • Randomized search CV
    Randomized search CV is used to automate the tuning of hyperparameters similar to Grid search CV. Randomized search CV is provided for a random search, and Grid search CV is provided for a grid search.
  • K fold cross-validation
    K-fold cross-validation is a way in ML to improve the holdout method. This method guarantees that our model’s score does not depend on how we picked the train and test set. The data set is divided into k number of subsets, and the holdout method is repeated k number of times.

Here, we will cover everything you need to know about SQL programming, such as DBMS, Normalization, Joins, etc.

• • • Introduction to DBMS

Database Management Systems (DBMS) is a software tool where you can store, edit, and organise data in your database. This module will teach you everything you need to know about DBMS.

• • • ER diagram

An Entity-Relationship (ER) diagram is a blueprint that portrays the relationship among entities and their attributes. This module will teach you how to make an ER diagram using several entities and their attributes.

• • • Schema design

Schema design is a schema diagram that specifies the name of record type, data type, and other constraints like primary key, foreign key, etc. It is a logical view of the entire database.

• • • Key constraints and basics of normalization

Key Constraints are used for uniquely identifying an entity within its entity set, in which you have a primary key, foreign key, etc. Normalization is one of the essential concepts in DBMS, which is used for organising data to avoid data redundancy. In this module, you will learn how and where to use all key constraints and normalization basics.

• • • Joins

As the name implies, a join is an operation that combines or joins data or rows from other tables based on the common fields amongst them. In this module, you will go through the types of joins and learn how to combine data.

• • • Subqueries involving joins and aggregations

This module will teach you how to work with subqueries/commands that involve joins and aggregations.

• • • Sorting

As the name suggests, Sorting is a technique to arrange the records in a specific order for a clear understanding of reported data. This module will teach you how to sort data in any hierarchy like ascending or descending, etc.

• • • Independent subqueries

The inner query that is independent of the outer query is known as an independent subquery. This module will teach you how to work with independent subqueries.

• • • Correlated subqueries

The inner query that is dependent on the outer query is known as a correlated subquery. This module will teach you how to work with correlated subqueries.

• • • Analytic functions

A function that determines values in a group of rows and generates a single result for every row is known as an Analytic Function.

• • • Set operations

The operation that combines two or more queries into a single result is called a Set Operation. In this module, you will implement various set operators like UNION, INTERSECT, etc.

• • • Grouping and filtering

Grouping is a feature in SQL that arranges the same values into groups using some functions like SUM, AVG, etc. Filtering is a powerful SQL technique, which is used for filtering or specifying a subset of data that matches specific criteria.