Description
About the Course
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Course Description
Machine Learning is a key to develop intelligent systems and analyze data in science and engineering. It covers theoretical foundations as well as essential algorithms for supervised and unsupervised learning. Classes on theoretical and algorithmic aspects are complemented by practical lab sessions.
What you will learn
- The Data Hype
- Python for data science introduction
- Plotting for exploratory data analysis (eda)
- Statistics and Probability Refresher, and Python
- Probability
Certification
- Certificate of completion from Kaizen Technologies.
Requirements
- Simple linear algebra
- Programming
- Calculus
Course Curriculum
Section 1:
- The Data Hype
- Big Data
- Data Science
- Machine Learning
Section 2:
Python For Data Science Introduction
- Installing Python and Anaconda
- Python For Data Science: Data Structures
- Python For Data Science: Functions
- Numpy and Pandas Library
- Matplotlib
- Python For Data Science: Computational Complexity
Section 3:
Plotting For Exploratory Data Analysis (Eda)
- Histogram and Introduction to PDF(Probability Density Function)
- Univariate Analysis using PDF
- CDF(Cumulative Distribution Function)
- Mean, Variance and Standard Deviation, Median
Section 4:
Statistics and Probability Refresher, and Python
- Mean, Median, Mode
- Using mean, median, and mode in Python
- Variation and Standard Deviation
- Bayes’ Theorem
Section 5:
Statistics
- Basics
- Variance
- Correlation
Section 6:
Predictive Models
- Linear Regression
- Polynomial Regression
Section 7
Machine Learning with Python
- Supervised vs. Unsupervised Learning, and Train/Test
- Using Train/Test to Prevent Overfitting a Polynomial Regression
- Bayesian Methods: Concepts
- Implementing a Spam Classifier with Naive Bayes K-Means Clustering
Section 8:
Apache Spark: Machine Learning on Big Data
- Introducing MLLib
- Decision Trees in Spark
- Using the Spark 2.0 DataFrame API for MLLib
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