Machine Learning Tutorial Python | Machi..

In this video series, we are going to learn about machine learning with Python. There are activities where computers are better than humans, and some activities where humans are better than computers. In simple words, machine learning is the technique that makes computers better at things than humans. It’s about making machines learn the things humans do. Machine learning is a bigger area, of which deep learning and mathematical models are important parts. One might think about why it is important to learn machine learning. It is because it has a big implication in real life too. Spam filtering in emails, voice assistance devices, and online video or music streaming recommendations, are a few of the real-life applications of machine learning which are already available to us. However, the best use of this feature can be expected in the near-decade in the form of driverless cars. Go through this machine learning playlist to understand these concepts more easily. This machine learning tutorials playlist includes:

1. linear regression
2. gradient descent
3. logistic regression
4. decision tree
5. support vector
6. K-fold cross-validation
7. KNN classification

The playlist also includes several practical implication projects which will help you get a more clear understanding of the concept. Another topic included in this machine learning course is Feature Engineering, which is an important part of data analytics and machine learning.

Hierarchical Clustering Course

Hierarchical clustering is a popular unsupervised machine learning algorithm used for grouping similar data points into clusters based on their similarity or dissimilarity. This course provides a comprehensive introduction to hierarchical clustering, covering both agglomerative and divisive approaches. The course begins with an overview of clustering and its applications, emphasizing the advantages and challenges of hierarchical clustering. You will learn about different distance metrics and linkage criteria used to measure the similarity between data points and how they impact the clustering results. Next, the course delves into agglomerative clustering, starting with individual data points as separate clusters and progressively merging them based on similarity. You will explore various linkage methods, such as single linkage, complete linkage, and average linkage, and understand their effects on cluster formation. The course also covers strategies for handling large datasets and techniques for visualizing the clustering results. The second part of the course focuses on divisive clustering, which starts with a single cluster and recursively splits it into smaller clusters. You will learn about top-down approaches like bisecting k-means and divisive hierarchical clustering algorithms. Throughout the course, you will gain practical experience by implementing hierarchical clustering algorithms using popular programming languages and libraries. Additionally, you will learn how to evaluate clustering results and interpret dendrograms, a visual representation of hierarchical clustering. By the end of the course, you will have a solid understanding of hierarchical clustering techniques, their implementation, and how to choose appropriate parameters to obtain meaningful clusters from your data.