Python With ML

This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.  You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN. 

With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms.  

What You'll Learn:

• Master Machine Learning on Python
• Make Powerful Analysis
• Make accurate predictions
• Make robust Machine Learning models
• Use Machine Learning for personal purpose
• Build an army of powerful Machine Learning models and know how to combine them to solve any problem
• Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
• Clean your input data to remove outliers

Syllabus:

• Machine learning with Python Basics
• Machine learning with python- Python Ecosystem
• Methods for machine learning
• Data Loading for ML Projects
• Understanding Data with statistics
• Understanding data with visualization
• Preparing Data
• Simple Linear Regression
• Multiple Linear Regression
• Supervised Learning
• Unsupervised Learning