Description
This course is designed for beginner to pro-level machine learning engineers. In this course, we will start from the very basics and will go to an advanced level. I have designed this course in a way that everybody can understand easily. Additionally, I have given tutorials for python,Numpy and matplotlib because these are very important for machine learning. In the end, we will do some practical projects and will apply all the concepts which we have learnt so far. We will see these concepts
- Linear Regression(Single Variable and Multiple Variable)
- Logistic Regression(Single Variable and Multiple Variable)
- Decision Tree
- Random Forest
- Naive-Bayes
- Support Vector Machine
- Ensemble Learning
- K-fold Method
- Unsupervised Learning
- Feature Engineering
- Deep Learning
- Outlier Detection
- One Hot Encoding
- Basics of python
- Basics of NumPy
- Basics of Pandas
- Basics of Data-Science
- Basics of matplotliband much more
Who this course is for:
- Anyone interested in Machine Learning.
- Students who have at least high school knowledge in math and who want to start learning Machine Learning.
- Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
- Any students in college who want to start a career in Data Science.
- Any data analysts who want to level up in Machine Learning.
- Any people who are not satisfied with their job and who want to become a Data Scientist.
- Any people who want to create added value to their business by using powerful Machine Learning tools.
