Machine learning Training
Course Description
Machine Learning allows applications to make smart decisions based on data. Applications of Machien Learning is many, including voice activated smart speakers, self drivng cars, predicitng what your customer wants to effective web search. No wonder folks are predicitng are comparing Machine Learning with the next Gold rush.
What will you learn?
In this training, attendees will learn the following:
- Python Refresher
- Python Libraries for Machine Learning
- Statistical Concepts
- Machine Learning
- Supervised vs Unsupervised Learning. Clustering, Classification and Categorization
- Deep Learning and Artitificial Intelligence
Attendees also learn:
- Resume' Preparation Guidelines and Tips
- Interview Preparation Guidelines and Tips
Pre-requisite
- No Pre-requisite. Some exposure to any type of programming could be useful.
Who should attend?
- Someone who wants to start his/her career as a Machine Learning Engineer or a Data Scientist
- Professionals who want to learn Machine Learning
Training Materials
All attendees would receive
- Training presentation of each session,
- Source Code for examples covered,
- Study Material,
- Home Work and Assignments.
What will be covered?
Python for Machine Learning - Part 1
- Python Editor Download and Installation
- Working with Python
- Python for Machine Learning
- Python Libraries
Python for Machine Learning - Part 2
- Data Types and Data Structures - Lists, Tuples, Range, Two Dimensional List, Dictionaries
- Control Statements
- Functions
Python for Machine Learning - Part 3
- NumPy Library
- Pandas Library
- MatPlotLib Library
Statistical Concepts
- Mean, Medium, Mode
- Mean, Medium, Mode in Python
- Variation, Standard Deviation
- Common Data Distributions
- Percentile and Moments
- Method Overriding and Overloading
- Covariance and Correlation
- MatPlotLib Library for Stastical Analysis
Supervised and Unsupervised Learning
- Clustering - KMean and Flat
- Scikit-learn Python Package
- Hierarchial Clustering
- Implementation and Best Practices
Predictive Model
- Linear Regression
- Polynomial Regression
- Multivariate Regression
- Multi Level Models
Supervised Learning
- Classification
- Regression
- k-Nearest neighbors classifiers
- Linear regression
Additional Machine Learning Topics
- Bayesian Methods: Concepts
- Naive Bayes
- Measuring Entropy
- K-Means Clustering
- Implementation and Best Practices
Capstone Project
- Implement Supervised and Unsupervised Learning on a Popular dataset
