easylearn.ing
Easy Learning with Machine Learning - Fundamental of Python Machine Learning
Development > Data Science
3.5 h
£19.99 £12.99
4.1 ★★★★★
43066 students

Enroll Now

Language: English

Python Machine Learning Mastery: From Beginner to Expert

What you will learn:

  • Data Analysis Techniques
  • Machine Learning Fundamentals
  • Python Programming for Machine Learning
  • Linear Regression
  • Polynomial Regression
  • Multiple Regression
  • Decision Trees
  • Confusion Matrices
  • Hierarchical Clustering
  • Logistic Regression
  • Model Evaluation
  • Grid Search
  • Categorical Data Handling
  • K-Means Clustering
  • Bootstrap Aggregation
  • Cross Validation
  • AUC-ROC Curves
  • K-Nearest Neighbors
  • Feature Engineering

Description

Ready to embark on a transformative journey into the world of machine learning with Python? Our comprehensive course, "Python Machine Learning Mastery: From Beginner to Expert," empowers you to grasp the core principles and practical applications of machine learning.


Machine learning is revolutionizing industries, from healthcare to finance, and Python is at the forefront of this exciting transformation. Whether you're a budding data scientist, an aspiring machine learning engineer, or simply curious about the potential of AI, this course provides the foundation you need to leverage the power of Python for machine learning.


Dive deep into key learning objectives:


  • Unlocking the World of Machine Learning: Gain a comprehensive understanding of machine learning concepts, its impact across various industries, and the crucial role Python plays in this field.


  • Mastering Python for Machine Learning: Acquire a robust foundation in Python programming, explore essential data structures, and delve into powerful libraries designed specifically for machine learning.


  • Building and Evaluating Effective Models: Learn to evaluate the performance of your machine learning models, compare their effectiveness, and select the optimal model for your specific tasks.


  • Feature Engineering: The Art of Data Enhancement: Discover the power of feature selection and engineering techniques to enhance the accuracy and performance of your machine learning models.


Why Choose This Course?


  • Comprehensive Curriculum: Our course is designed to guide you from novice to proficient machine learning practitioner, ensuring you develop a deep understanding of the fundamentals.


  • Hands-On Learning: Enhance your skills through engaging coding exercises, hands-on projects, and machine learning challenges that simulate real-world scenarios.


  • Expert Guidance: Benefit from the invaluable insights of experienced instructors who have extensive experience in machine learning projects and are dedicated to sharing their knowledge.


  • Lifetime Access: Enroll once and gain lifetime access to the course materials, ensuring you stay up-to-date with the latest advancements in machine learning.


Unleash the potential of Python in the world of machine learning. Enroll in "Python Machine Learning Mastery: From Beginner to Expert" today and acquire the essential knowledge and skills to excel in this exciting field. Don't miss this opportunity to become a proficient machine learning practitioner!


Your journey to mastering machine learning with Python starts now!

Curriculum

Introduction to Data Analysis and Machine Learning

This section provides a comprehensive foundation in data analysis techniques and introduces the fundamental concepts of machine learning. You'll explore topics like calculating mean, median, and mode, understanding standard deviation, and working with percentiles and data distributions. The section delves into visual data representation with scatter plots and introduces the fundamental concept of regression analysis, covering linear, polynomial, and multiple regressions. You'll also gain an understanding of data scaling techniques and learn how to prepare data for machine learning models.

Model Building and Evaluation

This section dives into the core of machine learning model building. You'll learn how to split data into training and testing sets, a crucial step for evaluating model performance. You'll explore the concept of decision trees, a powerful tool for making predictions based on data, and understand how to interpret the results using confusion matrices. The section covers hierarchical clustering, a method for grouping similar data points, and introduces logistic regression, a powerful technique for predicting categorical outcomes. You'll also learn about grid search, a technique for optimizing model hyperparameters, and explore methods for handling categorical data.

Advanced Machine Learning Techniques

This section introduces advanced machine learning techniques that enhance model accuracy and robustness. You'll explore K-Means clustering, an unsupervised learning algorithm for grouping data points based on similarity, and delve into the concept of Bootstrap Aggregation, a method for improving model stability and reducing variance. The section covers Cross Validation, a technique for estimating model performance on unseen data, and explains the importance of AUC-ROC curves for evaluating model performance on classification tasks. Finally, you'll explore the K-nearest neighbors algorithm, a non-parametric method for making predictions based on nearby data points.