Introduction to Machine Learning

Course overview

Uplatz offers this comprehensive course on Machine Learning with Python. It is a self-paced course consisting of video tutorials. You will be awarded Course Completion Certificate at the end of the course.

Machine Learning is one of the newest skills to emerge in the last decade, altering fields from consumer electronics and healthcare to retail. This has led to strong inquisitiveness about the industry among many students and working professionals. Machine learning has become an essential part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive investigation teams. If you are learning machine learning with Python, even as a beginner, this course will explain you ways to build your personal machine learning solutions. With all the data available today, machine learning applications are restricted only by your thoughts.

Machine learning is the kind of programming which gives computers the capability to automatically learn from data without being openly programmed. This means in other words that these programs change their performance by learning from data. For a software developer or a business analyst it is important to you might be curious about how machine learning can change the way you work and take your career to the next level. This machine learning tutorial will help you achieve your goals by becoming an expert in machine learning.

After successful completion of this course you will be able to:

• Learn supervised and unsupervised learning algorithms
• Learn linear regression to logistic regression to k-means clustering to random forest and other decision tree techniques
• Using Pandas and NumPy to accomplish various data mining and data wrangling tasks to turn your data into useable training data
• Learn to use scikit-learn
• Comb over your available data and implement practical machine learning techniques
• Learn to use computer science techniques
• Build the foundation of artificial intelligence, big data, and predictive models
• Build basic deep neural networks
• Learning reinforcement and deep learning in machines