Overview:

Robusta's Machine Learning course will make you an expert in machine learning, a form of artificial intelligence that automates data analysis to enable compute rs to learn and adapt through experience to do specific tasks without explicit programming. You'll master machine learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for the role of a Machine Learning Engineer.

Duration:

40 hours

Course outlines:

1.      Machine Learning

• Course Introduction
• Introduction to AI and Machine Learning
• Data Preprocessing
• Supervised Learning
• Feature Engineering
• Supervised Learning Classification
• Unsupervised Learning
• Time Series Modeling
• Ensemble Learning
• Recommender Systems
• Text Mining
• Project Highlights

2.      Data Science with Python:

• Course Overview
• Data Science Overview
• Data Analytics Overview
• Statistical Analysis and Business Applications
• Python Environment Setup and Essentials
• Mathematical Computing with Python (NumPy)
• Scientific computing with Python (Scipy)
• Data Manipulation with Pandas
• Machine Learning with Scikit–Learn
• Natural Language Processing with Scikit Learn
• Data Visualization in Python using matplotlib
• Web Scraping with BeautifulSoup
• Python integration with Hadoop MapReduce and Spark

3.      Math Refresher

4.      Statistics Essential for Data Science

• Introduction
• Sample or population data
• The fundamentals of descriptive statistics
• Measures of central tendency, asymmetry, and variability
• Practical example descriptive statistics
• Distributions
• Estimators and Estimates
• Confidence intervals advanced topics
• Practical example inferential statistics
• Hypothesis testing Introduction
• Hypothesis testing Let's start testing!
• Practical example hypothesis testing
• The fundamentals of regression analysis
• Subtleties of regression analysis
• Assumptions for linear regression analysis
• Dealing with categorical data
• Practical example regression analysis