Machine Learning - Intermediate 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:

  • Intermediate-Advanced programming skills with Python
  • In-Depth understanding of Machine Learning algorithms (Regression, SVM, Decision Tree, K-Nearest Neighbor
  • Knowledge and practical skills on how to analyze, pre-process, visualize data using advanced tools and libraries
  • Understanding and hands-on experience in how to build Machine learning models, train the models with real datasets and apply them to solve practical problems (predictions of future outputs, classifications of future samples,…)
  • Methods to calculate, visualize model errors, and evaluate the built Machine learning models
  • Ability to work for Machine Learning project

Requirements

  • Machine Learning – Beginner Level
  • Data Science – Beginner Level
  • Data Science – Intermediate Level
  • Programming with Python

Curriculum For This Course

  • Introduction to machine learning
  • Basic programming in Python.
    • Basic Python syntax
      • 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
    1. Complex data structures in Python:
      1. List
      2. Dictionary
      3. Tuple
    2. Advanced data structure in Python:
      1. Numpy Array
      2. Pandas DataFrame.
    3. Practice exercises
    1. Basic Data Visualization
    2. Matplotlib library
      1. Figure function
      2. Plot function
      3. Show function
    3. Scatter Plot, Line Plot, Bar Plot
    4. Implementation of those plots with Python/R
    5. Practice exercises
    1. Introduction to regression problem
    2. Simple linear regression model
    3. Least square method
    4. Mean squared errors
    1. Multiple linear regression model
    2. Gradient descent method
    3. Model evaluation with train/test split
    1. Pick a dataset out of 3 themes: Sport, Real Estate, Academics
    2. Exploring and Analyzing the dataset using data visualization techniques:
      1. Scatter Plot
      2. Line Plot
      3. Bar Plot
      4. Bubble Plot
      5. Box Plot
      6. Histogram
    3. Define a problem and use regression model to solve
    4. Evaluate the models
    1. Introduction to classification problems
    2. Decision tree model
    3. Gini criteria
    4. Implementation of decision tree model
    1. Evaluation of models using k-fold Cross-validation
    2. Leave-one-out method
    3. Implementation of evaluation of decision tree with k-fold Cross-validation
    1. K-nearest neighbors model
    2. Hyperparameter tuning of k
    3. Implementation of k-nearest neighbors model
    4. Implementation of evaluation of k-nearest neighbors with k-fold Cross-validation
    1. Support Vector Machine model
    2. Implementation of support vector machine model
    3. Implementation of evaluation of support vector machine with k-fold Cross-validation
    1. Logistic Regression model
    2. Implementation of logistic regression model
    3. Implementation of evaluation of logistic regression with k-fold Cross-validation
    1. Pick a dataset from our dataset bank
    2. Using learned knowledge to perform analysis on the dataset.
    3. Make a machine learning model
    4. Evaluate the machine learning model

UPCOMING COURSES/WORKSHOPS

Group name Start date Session duration Number of sessions Standard price
Machine Learning Intermediate Level (Group 1) - Limited Time Offer! Apr 5 (Fri) 10:00 - 12:00 2h 10 sessions (weekly) 60,000 JPY Register
Machine Learning Intermediate Level (Group 2) - Limited Time Offer! Apr 6 (Sat) 10:00 - 12:00 2h 10 sessions (weekly) 60,000 JPY Register

LOOKING FOR OTHER OPTIONS?

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FEATURED MENTOR

Tokyo Techies Lecturer

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 results-oriented 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.