Deep Dive into Machine Learning
Learn about each technique or algorithm used in the Machine Learning domain for building models, solving real-world problems, and delving into one of the most exciting technological domains in the twenty-first century.
Supervised Learning
Learn about Supervised Learning, one of the most widely used Machine Learning techniques. This technique requires trained data and input-output pairs, so are familiar with all aspects and master the technique.
Linear Regression
Learn about Linear Regression, a simple yet powerful algorithm for determining variable relationships. It should be noted that Linear Regression is li
Locally Weighted Regression
Learn about Locally Weighted Regression, which differs from Linear Regression in its non parametric approach. Learn about the mathematics that is invo
Logistic Regression
Understand Logistic Regression with its mathematical concepts, such as the Lost Function and decision surfacing.
K-Nearest Neighbors (KNN)
Learn about K Nearest Neighbor, a popular non-parametric Machine Learning algorithm with applications.
Naive Bayes
Understand the concept of Naive Bayes classification, as well as its implementation, types, and real-world applications.
Decision Trees
Learn about decision trees, which are an important component of machine learning and prediction algorithms. Flowchart-like structures with nodes and l
Random Forest
Random Forest methods were a huge breakthrough in regression and classification difficulties. For complex machine learning issues, a combination of de
Support Vector Machine (SVM)
Support Vector Machines are supervised machine learning algorithms that are particularly efficient for large numbers of samples. Learn how to put it i
Boosting Algorithm
A critical supervised learning algorithm that, as the name implies, boosts the accuracy and overall performance of the models by reducing variance and
Unsupervised Learning
This is in contrast to Supervised Learning, which does not use trained data but has a long list of real-world applications and is widely used by many tech companies.
Clustering
Clustering is a prominent unsupervised learning approach. It allows users to classify and group certain data points, resulting in more distinct and ac
Evaluating Classification Models Performance
Creating and deploying models isn't the only job of a data scientist or machine learning engineer. It is also necessary to analyse the performance of
Reinforcement Learning
Another machine learning technique that aids in achieving a better and more accurate output is reinforcement learning. This strategy improves a model's decision-making and decision-making path.
Important Differences
Machine learning has many subcategories and techniques, each with some similarities and differences. It is critical to understand how these algorithms differ in order to make better decisions when dealing with classification or regression problems. One of the most significant distinctions is between supervised and unsupervised learning, technique, application, limitations, and data.
Applications of Machine Learning
Discover how Machine Learning and its algorithms are used to solve real-world problems. Machine Learning has enabled everything from weather prediction to recommendation systems to self-driving cars.
Top Problems related to Deep Dive into Machine Learning
Symmetric Tree
Is It A Tree?
Spiral Order Traversal of a Binary Tree
Count Special Nodes In Generic Tree
Leftmost & Rightmost Nodes of Binary Tree
Triplets in Binary Tree
Count Univalue Subtrees
Binary Tree Pruning
Deepest Right Leaf
Reverse Alternate Nodes
Right View
Diagonal Sum
LCA of three Nodes
Replace Node With Depth
Diagonal Anagram
Except Boundary
Deepest Leaves Sum
Partial BST
Convert Bst To The Greater Sum Tree
Distance between two nodes of a Tree
Height of the Binary Tree From Inorder and Level Order Traversal
