課程介紹

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a s

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哪些人適合這堂課？

• Anyone interested in Machine Learning.
• Students who have at least high school knowledge in math and who want to start learning Machine Learning.
• Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
• Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
• Any students in college who want to start a career in Data Science.
• Any data analysts who want to level up in Machine Learning.
• Any people who are not satisfied with their job and who want to become a Data Scientist.
• Any people who want to create added value to their business by using powerful Machine Learning tools.

學習目標

• Master Machine Learning on Python & R
• Have a great intuition of many Machine Learning models
• Make accurate predictions
• Make powerful analysis
• Make robust Machine Learning models
• Create strong added value to your business
• Use Machine Learning for personal purpose
• Handle specific topics like Reinforcement Learning, NLP and Deep Learning
• Handle advanced techniques like Dimensionality Reduction
• Know which Machine Learning model to choose for each type of problem
• Build an army of powerful Machine Learning models and know how to combine them to solve any problem

課程介紹

Are you ready to start your path to becoming a Data Scientist! 

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!

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哪些人適合這堂課？

• This course is meant for people with at least some programming experience

學習目標

• Use Python for Data Science and Machine Learning
• Use Spark for Big Data Analysis
• Implement Machine Learning Algorithms
• Learn to use NumPy for Numerical Data
• Learn to use Pandas for Data Analysis
• Learn to use Matplotlib for Python Plotting
• Learn to use Seaborn for statistical plots
• Use Plotly for interactive dynamic visualizations
• Use SciKit-Learn for Machine Learning Tasks
• K-Means Clustering
• Logistic Regression
• Linear Regression
• Random Forest and Decision Trees
• Natural Language Processing and Spam Filters
• Neural Networks
• Support Vector Machines

課程介紹

The Problem

Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketpl

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哪些人適合這堂課？

• You should take this course if you want to become a Data Scientist or if you want to learn about the field
• This course is for you if you want a great career
• The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills

學習目標

• The course provides the entire toolbox you need to become a data scientist
• Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
• Impress interviewers by showing an understanding of the data science field
• Learn how to pre-process data
• Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
• Start coding in Python and learn how to use it for statistical analysis
• Perform linear and logistic regressions in Python
• Carry out cluster and factor analysis
• Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
• Apply your skills to real-life business cases
• Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
• Unfold the power of deep neural networks
• Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
• Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations

課程介紹

Learn R Programming by doing!

There are lots of R courses and lectures out there. However, R has a very steep learning curve and students often get overwhelmed. This course is different!

This course is truly step-by-step. In every new tutorial we b

…[更多細節]

哪些人適合這堂課？

• This course is for you if you want to learn how to program in R
• This course is for you if you are tired of R courses that are too complicated
• This course is for you if you want to learn R by doing
• This course is for you if you like exciting challenges
• You WILL have homework in this course so you have to be prepared to work on it

學習目標

• Learn to program in R at a good level
• Learn how to use R Studio
• Learn the core principles of programming
• Learn how to create vectors in R
• Learn how to create variables
• Learn about integer, double, logical, character and other types in R
• Learn how to create a while() loop and a for() loop in R
• Learn how to build and use matrices in R
• Learn the matrix() function, learn rbind() and cbind()
• Learn how to install packages in R
• Learn how to customize R studio to suit your preferences
• Understand the Law of Large Numbers
• Understand the Normal distribution
• Practice working with statistical data in R
• Practice working with financial data in R
• Practice working with sports data in R

課程介紹

Extremely Hands-On… Incredibly Practical… Unbelievably Real!

This is not one of those fluffy classes where everything works out just the way it should and your training is smooth sailing. This course throws you into the deep end.

In this course

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哪些人適合這堂課？

• Anybody with an interest in Data Science
• Anybody who wants to improve their data mining skills
• Anybody who wants to improve their statistical modelling skills
• Anybody who wants to improve their data preparation skills
• Anybody who wants to improve their Data Science presentation skills

學習目標

• Successfully perform all steps in a complex Data Science project
• Create Basic Tableau Visualisations
• Perform Data Mining in Tableau
• Understand how to apply the Chi-Squared statistical test
• Apply Ordinary Least Squares method to Create Linear Regressions
• Assess R-Squared for all types of models
• Assess the Adjusted R-Squared for all types of models
• Create a Simple Linear Regression (SLR)
• Create a Multiple Linear Regression (MLR)
• Create Dummy Variables
• Interpret coefficients of an MLR
• Read statistical software output for created models
• Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models
• Create a Logistic Regression
• Intuitively understand a Logistic Regression
• Operate with False Positives and False Negatives and know the difference
• Read a Confusion Matrix
• Create a Robust Geodemographic Segmentation Model
• Transform independent variables for modelling purposes
• Derive new independent variables for modelling purposes
• Check for multicollinearity using VIF and the correlation matrix
• Understand the intuition of multicollinearity
• Apply the Cumulative Accuracy Profile (CAP) to assess models
• Build the CAP curve in Excel
• Use Training and Test data to build robust models
• Derive insights from the CAP curve
• Understand the Odds Ratio
• Derive business insights from the coefficients of a logistic regression
• Understand what model deterioration actually looks like
• Apply three levels of model maintenance to prevent model deterioration
• Install and navigate SQL Server
• Install and navigate Microsoft Visual Studio Shell
• Clean data and look for anomalies
• Use SQL Server Integration Services (SSIS) to upload data into a database
• Create Conditional Splits in SSIS
• Deal with Text Qualifier errors in RAW data
• Create Scripts in SQL
• Apply SQL to Data Science projects
• Create stored procedures in SQL
• Present Data Science projects to stakeholders

課程介紹

New! Updated for 2021 with extra content on generative models: variational auto-encoders (VAE’s) and generative adversarial models (GAN’s)

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google

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哪些人適合這堂課？

• Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course.
• Technologists curious about how deep learning really works
• Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you’ll need some prior experience in coding or scripting to be successful.
• If you have no prior coding or scripting experience, you should NOT take this course – yet. Go take an introductory Python course first.

學習目標

• Build artificial neural networks with Tensorflow and Keras
• Classify images, data, and sentiments using deep learning
• Make predictions using linear regression, polynomial regression, and multivariate regression
• Data Visualization with MatPlotLib and Seaborn
• Implement machine learning at massive scale with Apache Spark’s MLLib
• Understand reinforcement learning – and how to build a Pac-Man bot
• Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
• Use train/test and K-Fold cross validation to choose and tune your models
• Build a movie recommender system using item-based and user-based collaborative filtering
• Clean your input data to remove outliers
• Design and evaluate A/B tests using T-Tests and P-Values

課程介紹

Learn Python Programming by doing!

There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different!

This course is truly step-by-step. In every ne

…[更多細節]

哪些人適合這堂課？

• This course if for you if you want to learn how to program in Python
• This course is for you if you are tired of Python courses that are too complicated
• This course is for you if you want to learn Python by doing
• This course is for you if you like exciting challenges
• You WILL have homework in this course so you have to be prepared to work on it

學習目標

• Learn to program in Python at a good level
• Learn how to code in Jupiter Notebooks
• Learn the core principles of programming
• Learn how to create variables
• Learn about integer, float, logical, string and other types in Python
• Learn how to create a while() loop and a for() loop in Python
• Learn how to install packages in Python
• Understand the Law of Large Numbers

課程介紹

Is statistics a driving force in the industry you want to enter? Do you want to work as a Marketing Analyst, a Business Intelligence Analyst, a Data Analyst, or a Data Scientist?

Well then, you’ve come to the right place!  

Statistics for Data Scie

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哪些人適合這堂課？

• People who want a career in Data Science
• People who want a career in Business Intelligence
• Business analysts
• Business executives
• Individuals who are passionate about numbers and quant analysis
• Anyone who wants to learn the subtleties of Statistics and how it is used in the business world
• People who want to start learning statistics
• People who want to learn the fundamentals of statistics

學習目標

• Understand the fundamentals of statistics
• Learn how to work with different types of data
• How to plot different types of data
• Calculate the measures of central tendency, asymmetry, and variability
• Calculate correlation and covariance
• Distinguish and work with different types of distributions
• Estimate confidence intervals
• Perform hypothesis testing
• Make data driven decisions
• Understand the mechanics of regression analysis
• Carry out regression analysis
• Use and understand dummy variables
• Understand the concepts needed for data science even with Python and R!

課程介紹

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most

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哪些人適合這堂課？

• Anyone interested in becoming a Data Scientist

學習目標

• Program in R
• Use R for Data Analysis
• Create Data Visualizations
• Use R to handle csv,excel,SQL files or web scraping
• Use R to manipulate data easily
• Use R for Machine Learning Algorithms
• Use R for Data Science

課程介紹

課程目標/學習目標：

1. 學習利用Python寫網路爬蟲、數據分析、資料預測走向、資料視覺化等程式與功能

2. 學習利用課堂中學習的Python程式解決經典問題，例如影像分析、分類與生存預測等

3. 學習利用資料科學解決現實問題

課程特色：

1. 使用線上Colab程式撰寫與資料分析平台

2. 以實際資料為案例說明與練習

3. 以口頭敘述情境和問題為導向之教學

教材影音時數：

至少6小時

適用對象：

1. 對資料科學感興趣的人

2. 對Python、爬

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哪些人適合這堂課？

• 對資料科學感興趣的人
• 對Python、爬蟲、資料分析、機器學習和深度學習感興趣的學習者
• 對資料科學有興趣的自主學習者

學習目標

• 學習利用Python寫網路爬蟲、數據分析、資料預測走向、資料視覺化等程式與功能
• 學習利用課堂中學習的Python程式解決經典問題，例如影像分析、分類與生存預測等
• 學習利用資料科學解決現實問題