Sr. Data Scientist at TCS and a researcher in data science, machine learning and python.
Machine Learning Course Content
Machine Learning course contains topics such as Linear Regression, Logistic Regression , Classification and Clustering algorithms and these algorithm use mathematical model that is being used for data analysis and future prediction.
here, I am going to guide you about the best machine learning course where
you can get practical knowledge and work on real-time projects to upgrade technical skills.
Machine Learning algorithms learn from data, the key idea about machine learning algorithm is to divide the data into two parts training and test. machine learning experts use train data to trained machine learning models using input and output variables and finally test these predictive models using test dataset.
Machine Learning Course Essential Topics.
- Python Programming.
- Matplotlib and Seaborn.
- Linear Regression
- Polynomial Regression.
- Logistic Regression.
- Random Forest.
- KNN Algorithm.
- Decision Trees.
- Naive Bayes Algorithm.
- Ensemble Learning.
- K-Means Clustering.
- Agglomerative Clustering.
- Advance Machine Learning Model.
- Principle component Analysis.
- Cross Validation.
- Cost Function for various models.
Types of Machine Learning
Basically, there are two types of machine learning algorithms, first supervised and second unsupervised machine learning. let use see what is the difference between supervised and unsupervised learning. supervised ML model use input and output variable to train model and predict output variable and unsupervised ML model only use input variable to train model and produce output variable.
Let me explain this concept by an example say you have a data having two column x, y where x is input variable and y is output variable. in supervised machine learning we fit x , y into our model and our model will create a mathematical equation y' = m * x + b that is used to predict future values.
Unsupervised machine leaning algorithm do not use y variable to create mathematical equation and use only (x) input variables. these types of algorithm used in exploratory data mining or clustering.
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