Essential Machine Learning Algorithms for Data Scientists

Essential Machine Learning Algorithms for Data Scientists, available at Free, has an average rating of 4.64, with 46 lectures, based on 7 reviews, and has 663 subscribers.

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You will learn about Gain expertise in core ML concepts: supervised/unsupervised learning, classification, regression, clustering, and feature engineering. Implement various ML algorithms: linear/logistic regression, decision trees, random forests, SVM, KNN, and neural networks. Evaluate models using accuracy, precision, recall, F1-score; fine-tune hyperparameters for improved performance. Apply theoretical knowledge to real-world problems through hands-on projects, covering data preprocessing, model training, and deployment. This course is ideal for individuals who are Data science enthusiasts eager to dive into machine learning and expand their knowledge. or Analysts seeking to apply machine learning techniques to extract insights from data. or Professionals transitioning into data science roles or looking to upskill in machine learning. or Students and researchers interested in understanding the theory and practical implementation of machine learning algorithms. It is particularly useful for Data science enthusiasts eager to dive into machine learning and expand their knowledge. or Analysts seeking to apply machine learning techniques to extract insights from data. or Professionals transitioning into data science roles or looking to upskill in machine learning. or Students and researchers interested in understanding the theory and practical implementation of machine learning algorithms.

Enroll now: Essential Machine Learning Algorithms for Data Scientists

Summary

Title: Essential Machine Learning Algorithms for Data Scientists

Price: Free

Average Rating: 4.64

Number of Lectures: 46

Number of Published Lectures: 46

Number of Curriculum Items: 46

Number of Published Curriculum Objects: 46

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Gain expertise in core ML concepts: supervised/unsupervised learning, classification, regression, clustering, and feature engineering.

Implement various ML algorithms: linear/logistic regression, decision trees, random forests, SVM, KNN, and neural networks.

Evaluate models using accuracy, precision, recall, F1-score; fine-tune hyperparameters for improved performance.

Apply theoretical knowledge to real-world problems through hands-on projects, covering data preprocessing, model training, and deployment.

Who Should Attend

Data science enthusiasts eager to dive into machine learning and expand their knowledge.

Analysts seeking to apply machine learning techniques to extract insights from data.

Professionals transitioning into data science roles or looking to upskill in machine learning.

Students and researchers interested in understanding the theory and practical implementation of machine learning algorithms.

Target Audiences

Data science enthusiasts eager to dive into machine learning and expand their knowledge.

Analysts seeking to apply machine learning techniques to extract insights from data.

Professionals transitioning into data science roles or looking to upskill in machine learning.

Students and researchers interested in understanding the theory and practical implementation of machine learning algorithms.

Are you ready to unlock the power of machine learning and elevate your data science skills? Welcome to “Machine Learning Algorithms for Data Scientists,” a comprehensive course designed to equip you with the knowledge and practical skills needed to excel in the field of data science.

Introduction to ML In this introductory section, we’ll lay the foundation for your journey into machine learning. You’ll gain an understanding of the types of machine learning, including supervised and unsupervised learning, setting the stage for deeper exploration.

Linear Regression Delve into linear regression, a fundamental algorithm for predictive modeling. Learn how to evaluate linear regression models and witness its application through a hands-on demonstration. By the end of this module, you’ll grasp the intricacies of linear regression and its significance in data science.

Logistic Regression Explore logistic regression, a powerful tool for binary classification tasks. From model training to prediction, you’ll discover the nuances of logistic regression and its regularization techniques. Get ready to harness the predictive power of logistic regression for various real-world applications.

Decision Trees Uncover the versatility of decision trees in data analysis. Learn how to handle missing data, explore decision tree algorithms through practical demonstrations, and evaluate their pros and cons. Gain insights into decision tree applications across diverse domains.

Random Forests Dive into the world of ensemble learning with random forests. Master hyperparameter tuning, witness the feature selection capabilities of random forests, and understand their limitations. By the end of this module, you’ll be equipped to leverage random forests for robust predictive modeling.

Support Vector Machines (SVM) Unlock the potential of support vector machines for classification and regression tasks. Through hands-on demos, you’ll learn to handle imbalanced datasets, evaluate SVM performance, and harness SVM’s capabilities for data-driven insights.

Naive Bayes Discover the simplicity and effectiveness of Naive Bayes classifiers. Explore their applications, learn the essentials of training a Naive Bayes model, and weigh their pros and cons for different use cases.

K-Nearest Neighbors (KNN) Delve into the intuitive approach of K-Nearest Neighbors for classification and regression. Understand distance metrics, witness KNN in action through a practical demonstration, and grasp its significance in pattern recognition tasks.

Clustering Algorithms Embark on a journey into clustering algorithms, including K-means and hierarchical clustering. Learn how to evaluate clustering results, explore real-world applications, and understand the role of clustering in unsupervised learning.

Enroll now in “Machine Learning Algorithms for Data Scientists” and unlock the keys to mastering essential machine learning techniques. Whether you’re a beginner or seasoned professional, this course will empower you to tackle real-world data science challenges with confidence. Let’s embark on this transformative learning journey together!

Course Curriculum

Chapter 1: Introduction to ML

Lecture 1: Introduction

Lecture 2: Types of Machine Learning

Lecture 3: Supervised Vs Unsupervised Learning

Lecture 4: Summary

Chapter 2: Linear Regression

Lecture 1: Linear Regression

Lecture 2: Evaluating Linear Regression

Lecture 3: Demo: Linear Regression

Lecture 4: Summary

Chapter 3: Logistic Regression

Lecture 1: Logistic Regression

Lecture 2: Evaluating Logistic Regression

Lecture 3: Training & Prediction with Linear Regression

Lecture 4: Training & Prediction with Linear Regression

Lecture 5: Summary

Chapter 4: Decision Trees

Lecture 1: Decision Trees

Lecture 2: Handling Missing Values in Decision Trees

Lecture 3: Demo: Decision Trees

Lecture 4: Pros and Cons

Lecture 5: Applications of Decision Trees

Lecture 6: Summary

Chapter 5: Random Forests

Lecture 1: Random Forests

Lecture 2: Tuning hyperparameters

Lecture 3: Demo: Random Forests

Lecture 4: Feature selection in random forests

Lecture 5: Limitations of random forests

Lecture 6: Summary

Chapter 6: Support Vector Machines (SVM)

Lecture 1: Support Vector Machines (SVM)

Lecture 2: Demo: SVM

Lecture 3: Handling Imbalanced Datasets with SVM

Lecture 4: Evaluating SVM Performance

Lecture 5: Summary

Chapter 7: Naive Bayes

Lecture 1: Naive Bayes

Lecture 2: Applications of Naive Bayes

Lecture 3: Training Naive Bayes Classifier

Lecture 4: Pros and Cons

Lecture 5: Summary

Chapter 8: K-Nearest Neighbors (KNN)

Lecture 1: K-Nearest Neighbors (KNN)

Lecture 2: Distance metrics in KNN

Lecture 3: Demo: KNN

Lecture 4: Summary

Chapter 9: Clustering Algoritims

Lecture 1: K-means clustering

Lecture 2: Demo: K-means clustering

Lecture 3: Hierarchical clustering

Lecture 4: Demo: Hierarchical Clustering

Lecture 5: Evaluating clustering results

Lecture 6: Applications of clustering

Lecture 7: Summary

Instructors

Techjedi LLP
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