Clustering & Unsupervised Methods

Python for Machine Learning & Data Science Masterclass

The Python for Data Science and Machine Learning course is designed to equip learners with a comprehensive understanding of Python programming, data science techniques, and machine learning algorithms. Whether you are a beginner looking to enter the field or a seasoned professional seeking to expand your skillset, this course provides the knowledge and practical experience necessary to excel in the rapidly growing field of data science.Course Objectives:1. Master Python Programming: Develop a strong foundation in Python programming, including syntax, data structures, control flow, and functions. Gain proficiency in using Python libraries such as Num Py, Pandas, and Matplotlib to manipulate and visualize data effectively.2. Data Cleaning and Preprocessing: Learn how to handle missing data, outliers, and inconsistent data formats. Acquire skills in data cleaning and preprocessing techniques to ensure the quality and reliability of datasets.3. Exploratory Data Analysis: Understand the principles and techniques of exploratory data analysis. Learn how to extract insights, discover patterns, and visualize data using statistical methods and Python libraries.4. Statistical Analysis: Gain a solid understanding of statistical concepts and techniques. Apply statistical methods to analyze data, test hypotheses, and draw meaningful conclusions.5. Machine Learning Fundamentals: Learn the foundations of machine learning, including supervised and unsupervised learning, regression, classification, and clustering. Understand the strengths and limitations of different machine learning algorithms.6. Machine Learning Implementation: Gain hands-on experience in implementing machine learning models using Python libraries such as Scikit-Learn. Learn how to train, evaluate, and optimize machine learning models.7. Feature Engineering and Selection: Develop skills in feature engineering to create mea

Artificial Intelligence is the next digital frontier, with profound implications for business and society. The global AI market size is projected to reach $202.57 billion by 2026, according to Fortune Business Insights.This Data Science & Machine Learning (ML) course is not only ‘Hands-On’ practical based but also includes several use cases so that students can understand actual Industrial requirements, and work culture. These are the requirements to develop any high level application in AI. In this course several Machine Learning (ML) projects are included.1) Project - Customer Segmentation Using K Means Clustering2) Project - Fake News Detection using Machine Learning (Python)3) Project COVID-19: Coronavirus Infection Probability using Machine Learning4) Project - Image compression using K-means clustering | Color Quantization using K-Means This course include topics ---What is Data Science Describe Artificial Intelligence and Machine Learning and Deep Learning Concept of Machine Learning - Supervised Machine Learning , Unsupervised Machine Learning and Reinforcement Learning Python for Data Analysis- Numpy Working envirnment-Google Colab Anaconda Installation Jupyter Notebook Data analysis-Pandas Matplotlib What is Supervised Machine Learning Regression Classification Multilinear Regression Use Case- Boston Housing Price Prediction Save Model Logistic Regression on Iris Flower Dataset Naive Bayes Classifier on Wine Dataset Naive Bayes Classifier for Text Classification Decision TreeK-Nearest Neighbor(KNN) Algorithm Support Vector Machine Algor

This course is an exciting hands-on view of the fundamentals of Data Science and Machine Learning Data Science and Machine Learning are developing on a massive scale. Everywhere you look in society, the world wide web, or in technology, you will find Data Science and Machine Learning algorithms working behind the scenes to analyze and optimize all aspects of our lives, businesses, and our society. Data Science and Machine Learning with Artificial Intelligence are some of the hottest and fastest-developing areas right now. This course will teach you the fundamentals of Data Science and Machine Learning. This course has exclusive content that will teach you many new things regardless of if you are a beginner or an experienced Data Scientist, and aspires to be one of the best Udemy courses in terms of education and value.  You will learn about Regression and Prediction with Machine Learning models using supervised learning. This course has the most complete and fundamental master-level regression analysis content packages on Udemy, with hands-on, useful practical theory, and automatic Machine Learning algorithms for model building, feature selection, and artificial intelligence. You will learn about models ranging from linear regression models to advanced multivariate polynomial regression models.Classification with Machine Learning models using supervised learning. You will learn about the classification process, classification theory, and visualizations as well as some useful classifier models, including the very powerful Random Forest Classifier Ensembles and Voting Classifier Ensembles.Cluster Analysis with Machine Learning models using unsupervised learning. In this part of the course, you will learn about unsupervised learning, cluster theory, artificial intelligence, explorative data analysis, and seven useful Machine Learning clustering algorithms ranging from hierarchical cl

This course is for everyday people looking for an intuitive, beginner-friendly introduction to the world of machine learning and data science.Build confidence with guided, step-by-step demos, and learn foundational skills from the ground up. Instead of memorizing complex math or learning a new coding language, we'll break down and explore machine learning techniques to help you understand exactly how and why they work.Follow along with simple, visual examples and interact with user-friendly, Excel-based models to learn topics like linear and logistic regression, decision trees, KNN, naïve bayes, hierarchical clustering, sentiment analysis, and more – without writing a SINGLE LINE of code.This course combines 4 best-selling courses from Maven Analytics into a single masterclass:PART 1: Univariate & Multivariate ProfilingPART 2: Classification ModelingPART 3: Regression & ForecastingPART 4: Unsupervised LearningPART 1: Univariate & Multivariate Profiling In Part 1 we’ll introduce the machine learning workflow and common techniques for cleaning and preparing raw data for analysis. We’ll explore univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin & box plots, scatter plots, and correlation:Section 1: Machine Learning Intro & Landscape Machine learning process, definition, and landscape Section 2: Preliminary Data QA Variable types, empty values, range & count calculations, left/right

This is a hands-on, project-based course designed to help you master the foundations for unsupervised machine learning in Python.We’ll start by reviewing the Python data science workflow, discussing the techniques &amp; applications of unsupervised learning, and walking through the data prep steps required for modeling. You’ll learn how to set the correct row granularity for modeling, apply feature engineering techniques, select relevant features, and scale your data using normalization and standardization.From there we'll fit, tune, and interpret 3 popular clustering models using Scikit-Learn. We’ll start with K-Means Clustering, learn to interpret the output’s cluster centers, and use inertia plots to select the right number of clusters. Next, we’ll cover Hierarchical Clustering, where we’ll use dendrograms to identify clusters and cluster maps to interpret them. Finally, we’ll use DBSCAN to detect clusters and noise points and evaluate the models using their silhouette score.We’ll also use DBSCAN and Isolation Forests for anomaly detection, a common application of unsupervised learning models for identifying outliers and anomalous patterns. You’ll learn to tune and interpret the results of each model and visualize the anomalies using pair plots.Next, we’ll introduce the concept of dimensionality reduction, discuss its benefits for data science, and explore the stages in the data science workflow in which it can be applied. We’ll then cover two popular techniques: Principal Component Analysis, which is great for both feature extraction and data visualization, and t-SNE, which is ideal for data visualization.Last but not least, we’ll introduce recommendation engines, and you'll practice creating both content-based and collaborative filtering recommenders using techniques such as Cosine Similarity and Singular Value Decomposition.<

This is a crash course, but an in-depth course, which will develop you as a Machine learning specialist. Designed with solutions to real life life problems, this will be a boon for your ongoing projects and the organization you work for. Students, Professors and machine learning consultants will find the course interesting, hassle free and up-to-date. Surely, the students will be employable Machine Learning Engineers and data scientists. Given by an enthusiastic and expert professor after testing it in classrooms and projects several times. The students can carry out a number of projects using this course. This exemplary, engaging, enlightening and enjoyable course is organized as seven interesting modules, with abundant worked examples in the form of code executed on Jupyter Notebook. It is important that data is visualized before attempting to carryout machine learning and hence we start the course with a module on data visualization. This is followed by a full blown and enjoyable exposure to Regression covering simple linear regression, polynomial regression, multiple linear regression.&nbsp; Regression is followed by extensive discussions on another important supervised learning algorithms on Classification. We carry out modeling using classification strategies such as logistic regression, Naive Bayes classifier, support vector machine, K nearest neighbor, Decision trees, ensemble learning, classification and regression trees, random forest and boosting - ada boost, gradient boosting. From supervised learning we move on to discuss about unsupervised learning - clustering for unlabelled data. We study the hierarchical, k means, k medoids and Agglomerative Clustering. It is not enough to know the algorithms, but also strategies such as bias variance trade off and curse of dimensionality to be successful in this challenging field of current and futuristic importance. We also carry out Principal Component analysis and Linear discriminant analysis to de

This advanced machine learning and deep learning course will cover the following topics:SBERT and BERT: These are pre-trained models that are used for natural language processing tasks such as sentence classification, named entity recognition, and question answering.Sentence Embedding and Similarity Measures: Techniques for representing sentences as numerical vectors, and methods for comparing the similarity between sentences.Clustering: Algorithms for grouping similar data points together, such as k-means and hierarchical clustering.Text Summarization: Techniques for automatically generating a concise summary of a longer text.Question Answering: Techniques for automatically answering questions based on a given text.Image Clustering: Algorithms for grouping similar images together.Image Search: Techniques for searching for images based on their content.Throughout the course, students will work on hands-on projects that will help them apply the concepts they have learned to real-world problems. They will also get an opportunity to implement the latest state of the art techniques in the field to solve various NLP and CV problems.By the end of this course, your confidence will boost in creating and analyzing the Image and Text Processing ML model in Python. You'll have a thorough understanding of how to use Text Data and Image Data modeling to create predictive models and solve real-world business problems.How this course will help you?This course will give you a very solid foundation in machine learning. You will be able to use the concepts of this course in other machine learning models. If you are a business manager or an executive or a student who wants to learn and excel in machine learning, this is the perfect course for you.What makes us qualified to teach you?I am a Ph.D. Scholar

Are you looking for a Machine Learning and Deep Learning course explained in Tamil?This course is designed for Tamil-speaking learners who want to master AI, ML, and DL concepts from the basics to advanced with clear explanations and practical examples.Machine Learning and Deep Learning are at the core of Artificial Intelligence (AI) and are widely used in real-world applications such as speech recognition, computer vision, chatbots, healthcare, recommendation systems, and much more.In this A to Z Tamil course, we’ll cover everything step by step in simple Tamil explanations so that even beginners can understand complex concepts easily.What You’ll Learn in This Course Introduction to Machine Learning (ML) &amp; Artificial Intelligence (AI)Types of Machine Learning:Supervised Learning Unsupervised Learning Reinforcement LearningML Algorithms explained in Tamil:Linear &amp; Logistic Regression Decision Trees &amp; Random ForestsKNN &amp; Naive Bayes Clustering (K-Means, Hierarchical)Deep Learning Concepts Artificial Neural Networks (ANNs)Convolutional Neural Networks (CNNs)Recurrent Neural Networks (RNNs, LSTMs, GRUs)Transfer Learning &amp; Pretrained Models Why Take This Course?Explained 100% in Tamil – No confusion, easy to follow Covers both theory and practical insightsA to Z coverage of Machine Learning and Deep Learning Beginner-friendly with real-world exampl

This course is designed to cover maximum concepts of machine learning a-z. Anyone can opt for this course. No prior understanding of machine learning is required.Bonus introductions include Natural Language Processing and Deep Learning.Below Topics are covered&nbsp; Chapter - Introduction to Machine Learning- Machine Learning?- Types of Machine Learning Chapter - Setup Environment - Installing Anaconda, how to use Spyder and Jupiter Notebook- Installing Libraries Chapter - Creating Environment on cloud (AWS)- Creating EC2, connecting to EC2- Installing libraries, transferring files to EC2 instance, executing python scripts Chapter - Data Preprocessing- Null Values- Correlated Feature check- Data Molding- Imputing- Scaling- Label Encoder- On-Hot Encoder Chapter - Supervised Learning: Regression- Simple Linear Regression- Minimizing Cost Function - Ordinary Least Square(OLS), Gradient Descent- Assumptions of Linear Regression, Dummy Variable- Multiple Linear Regression- Regression Model Performance - R-Square- Polynomial Linear Regression Chapter - Supervised Learning: Classification- Logistic Regression- K-Nearest Neighbours- Naive Bayes- Saving and Loading ML Models- Classification Model Performance - Confusion Matrix Chapter: Un Supervised Learning: Clustering- Partitionaing Algorithm: K-Means Algorithm, Random Initialization Trap, Elbow Method- Hierarchical Clustering: Agglomerative, Dendogram- Density Based Clustering: DBSCAN- Measuring Un Supervised Clusters Performace - Silhouette Index Chapter: Un Supervised Learning: Association R