‘Machine Learning is all about how a machine with a synthetic intelligence learns like a human being’
Welcome to the course on Machine Learning and Implementing it using Python 3. Because the title says, this course recommends to have a primary data in Python 3 to understand the implementation half simply however it’s not obligatory.
This course has sturdy content material on the core ideas of ML such because it’s options, the steps concerned in constructing a ML Mannequin – Knowledge Preprocessing, Finetuning the Mannequin, Overfitting, Underfitting, Bias, Variance, Confusion Matrix and efficiency measures of a ML Mannequin. We’ll perceive the significance of many preprocessing strategies corresponding to Binarization, MinMaxScaler, Normal Scaler
We will implement many ML Algorithms in Python using scikit-learn library in a couple of traces. Can’t we? But, that received’t assist us to know the algorithms. Therefore, on this course, we’ll first look into understanding the arithmetic and ideas behind the algorithms after which, we’ll implement the identical in Python. We’ll additionally visualize the algorithms so as to make it extra attention-grabbing. The algorithms that we’ll be discussing on this course are:
1. Linear Regression
2. Logistic Regression
3. Help Vector Machines
4. KNN Classifier
5. KNN Regressor
6. Choice Tree
7. Random Forest Classifier
8. Naive Bayes’ Classifier
And so forth. We’ll be evaluating the outcomes of all of the algorithms and making a very good analytical method. What are you ready for?
Who this course is for:
- Newbie Python builders
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