PYTHON for Machine Learning Course Bundle - 39 Courses in 1 | 6 Mock Tests

Objectives

This course offers a deep and wide range of skills set from Programming to statistics and machine learning algorithms. The skills you will attain from this course could make you an expert Data Analyst, Quality Analyst and Business Analyst and Statistical Analyst roles.

Machine learning algorithms such as Regression, Clustering, Classification and prominent libraries such as Pandas, Matplotlib, SciKit -learn is covered from this course.

Course Highlights

This course covers all the topics from Mathematics to Programming to Visualization techniques that are needed for a Data Scientist role. The whole module that is provided is based on recent trends and growing job opportunities in the Data Science world.

• The course provides the module of Machine Learning with a prominent SciKit -Learn library. Algorithms like Regression, Clustering, and Classification are done using the SciKit-Learn library.
• Pandas library which is used prominently for Data Analysis, Data wrangling and analytics is covered extensively in this course.
• Data Visualization library Matplotlib is covered from Beginner to Advance level in this course it is very useful for pictorial representation and Data Reporting.
• PySpark library which is useful for processing different kinds of structured and unstructured datasets and also can be used in getting API for working on different format data.
• Seaborn library which can also be used for Data Visualization is covered from beginner to advanced level.
• AI and Statistics module using python is also covered in this course which is very useful in gaining core data science skills.
• Financial analytics using python is covered to provide a much needed and valuable knowledge on how Data Science is used in the financial platform.
• Video Analytics using Opencv (computer vision) library which helps analyze video format data is covered in this course which would be very helpful in gaining insights form Video data.

Project Highlights

• A project like Diabetes prediction is covered in this course with a real-time dataset to classify the patients who have the risk of getting diabetes from their historical medical data.
• Deep learning library TensorFlow is covered in this course for a project to build a Regression model.
• Complete Data Analytics and Exploratory analysis are covered as a project in this course. This project will provide the skill to examine and study a dataset to gain complete information before building any models.
• Project on Machine Learning algorithm called Random Forest which is a very widely used classification technique to get accurate results is also covered in this course.
• Projects on Linear regression which are used to predict the continuous value in a dataset are covered in this course. Linear Regression is a very popular and widely used algorithm to predict the continuous value in real-time data. Sales prediction is done using Linear regression.
• A very common ML project with a Titanic Survivor prediction using the Titanic dataset is worked out to gain a practical understanding of classification algorithms.
• A very important project that has been very popular in recent times known as Recommendation Systems which used in almost all the user interactive platforms from YouTube to Netflix is covered in this course. The recommendation system decides the sales of a product by making the customers choose from their likeness by studying their historical behavior and viewing patterns.
• A very important financial domain project that is very widely used in the banking sector known as credit card defaulter prediction and also the prediction of Housing price from a whole range of features of a house is practically worked out in this course.

On the whole, the course module provides Hands-on training and project experiences that will help you gain knowledge on the complete skill set that is required in the Data Science domain.