• views: 52
• date: 25 January 2026
• posted by: voska89

What you'll learn
Machine Learning Algorithms & Terminologies
Artificial Intelligence
Python Libraries - Numpy, Pandas, Scikit-learn, Matplotlib, Seaborn
Requirements
Yes, A Basic Knowledge in Python is preferred
Description
'Machine Learning is all about how a machine with an artificial intelligence learns like a human being'Welcome to the course on Machine Learning and Implementing it using Python 3. As the title says, this course recommends to have a basic knowledge in Python 3 to grasp the implementation part easily but it is not compulsory. This course has strong content on the core concepts of ML such as it's features, the steps involved in building a ML Model - Data Preprocessing, Finetuning the Model, Overfitting, Underfitting, Bias, Variance, Confusion Matrix and performance measures of a ML Model. We'll understand the importance of many preprocessing techniques such as Binarization, MinMaxScaler, Standard ScalerWe can implement many ML Algorithms in Python using scikit-learn library in a few lines. Can't we? Yet, that won't help us to understand the algorithms. Hence, in this course, we'll first look into understanding the mathematics and concepts behind the algorithms and then, we'll implement the same in Python. We'll also visualize the algorithms in order to make it more interesting. The algorithms that we'll be discussing in this course are:1. Linear Regression2. Logistic Regression3. Support Vector Machines4. KNN Classifier5. KNN Regressor6. Decision Tree7. Random Forest Classifier8. Naive Bayes' Classifier9. ClusteringAnd so on. We'll be comparing the results of all the algorithms and making a good analytical approach. What are you waiting for?
Beginner Python developers
Homepage

https://www.udemy.com/course/machine-learning-using-python-programming/

   
   
   

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