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Description
Welcome to comprehensive course on Machine Learning and Data Science for Everyone! This course is designed for those who are new to the field, as well as Machine Learning Engineers and Data Scientists looking to level up their skills or Students or Profession want to make a swift transition into this field .
In this course, we will delve into a wide array of essential topics:
Confusion Matrix & Analysis: Understand the performance of your machine learning model and improve its accuracy.
Types of Data Analytics: Learn the different types of data analytics and how they can be used to make effective predictions.
Simple Linear Regression: Discover the difference between supervised and unsupervised learning, and understand the concept of best fit, worst fit lines, and slopes.
Different types of Machine Learning Models their Limitations and Advantages and where to use which and their applications
Classification: Master techniques such as logistic regression, naive bayes, clustering, and numerical LDA. Understand distance metrics and scalers within matrices.
Exact Naive Bayes Classifier: Learn about probability, prediction, and K-nearest neighbors algorithms. Understand Euclidean, Manhattan, and Mahalanobis distances with numerical examples.
Overfitting & Underfitting: Learn about K-fold, early stopping, pruning, decision trees, regularization, and the bias-variance tradeoff and More etc.
Each topic will be covered in depth, accompanied by numerical exercises and examples to reinforce your understanding. By the end of this course, you will have a solid foundation in machine learning and data science, and be well on your way to becoming an expert in the field.
Whether you’re a beginner in data science and machine learning, or a seasoned professional looking to level up, this course has something for everyone to become a data science analytics engineer or machine learning professional etc. Join us on this exciting journey and take the first step towards a more impactful future in data science and machine learning!
Enroll now and let’s get started!
