Machine Learning  Beginner Level
Difficulty: ⭐️⭐️☆☆☆
Description
Machine learning is an important field of Artificial Intelligence by giving the machine the power to improve its performance by consumes more and more data. In this course, the students will learn the concept of machine learning, especially, supervised learning, and apply various machine learning algorithms in practical problems.
Learning outcomes:
By the end of this course, students will achieve:
 Basic programming with Python / R.
 Understanding of data structure.
 Skills to read, write, analyze a simple dataset.
 Skills to use regression model to predict continuous value
 Skills to use different classification model such as: logistic regression, decision tree, Support Vector Machines, KNearest Neighbors to predict data into categories.
 Skills to evaluate machine learning models.
Requirements
 Basic computer skills
Curriculum For This Course

 Introduction to machine learning
 Basic programming in Python.
 Comment
 print() command
 Basic data types
 Basic operators
 Basic control flow: if statement
 Variables and Variable Types
 Basic String manipulation
 Basic control flow: for loop
 Practice exercises

 Complex data structures in Python:
 List
 Dictionary
 Tuple
 Advanced data structure in Python:
 Numpy Array
 Pandas DataFrame.
 Practice exercises
 Complex data structures in Python:

 Basic Data Visualization
 Matplotlib library
 Figure function
 Plot function
 Show function
 Scatter Plot, Line Plot, Bar Plot
 Implementation of those plots with Python/R
 Practice exercises

 Introduction to regression problem
 Simple linear regression model
 Least square method
 Mean squared errors

 Multiple linear regression model
 Gradient descent method
 Model evaluation with train/test split

 Pick a dataset out of 3 themes: Sport, Real Estate, Academics
 Exploring and Analyzing the dataset using data visualization techniques:
 Scatter Plot
 Line Plot
 Bar Plot
 Bubble Plot
 Box Plot
 Histogram
 Define a problem and use regression model to solve
 Evaluate the models

 Introduction to classification problems
 Decision tree model
 Gini criteria
 Implementation of decision tree model

 Evaluation of models using kfold Crossvalidation
 Leaveoneout method
 Implementation of evaluation of decision tree with kfold Crossvalidation

 Knearest neighbors model
 Hyperparameter tuning of k
 Implementation of knearest neighbors model
 Implementation of evaluation of knearest neighbors with kfold Crossvalidation

 Support Vector Machine model
 Implementation of support vector machine model
 Implementation of evaluation of support vector machine with kfold Crossvalidation

 Logistic Regression model
 Implementation of logistic regression model
 Implementation of evaluation of logistic regression with kfold Crossvalidation


 Pick a dataset from our dataset bank
 Using learned knowledge to perform analysis on the dataset.
 Make a machine learning model
 Evaluate the machine learning model

UPCOMING COURSES/WORKSHOPS
Group name  Start date  Session duration  Number of sessions  Standard price 

LOOKING FOR OTHER OPTIONS?
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FEATURED MENTOR
Phong Nguyen
Head of Artificial Intelligence and Data Science Department
 Data Science and Research Mentor at Tokyo Techies
 Master Graduate from Carnegie Mellon University.
 Researcher in an AI lab at a big global corporation.
 Global working experience in Singapore, Australia, US, Vietnam and Japan.
 A resultsoriented Researcher and Data Guru with vast managerial and technical experience in financial management, marketing, business planning.
 Adapt at programming in multiple different languages, such as Java, Python and R.