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 Scaler
We 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 Regression
2. Logistic Regression
3. Support Vector Machines
4. KNN Classifier
5. KNN Regressor
6. Decision Tree
7. Random Forest Classifier
8. Naive Bayes’ Classifier
9. Clustering
And so on. We’ll be comparing the results of all the algorithms and making a good analytical approach. What are you waiting for?
Who this course is for:
- Beginner Python developers
Also check: Python Bootcamp
