Chia nhỏ theo 3 levels: DATA ANALYTICS - MACHINE LEARNING - DEEP LEARNING Phương pháp và học liệu phù hợp với mọi đối tượng!

1 - Data Analytics

R, Dataframe, Data processing, Data wrangling, Reporting, Statistics, Exploratory Data Analysis (EDA), Linear Regression, Logistics regression, Decision Tree, Random Forest, Clustering,…

2 - Machine Learning

Python, Pandas, Seaborn, Apriori, NLP, k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost, Recommender systems, SVM, Dimension reduction, Reinforcement Learning,...

3 - Deep Learning

Neural networks, Activation, Gradient Descent, Backpropagation, TensorFlow, Keras, Convolutional Neural Networks, (CNN), RNN, SOM, AutoEncoders, …

(4 - Business Intelligence)

Datawarehouse, ETL, SQL, datamart, cube, visualization, EDA, Dashboard, storytelling, Tableau, PowerBI,…

(5 - Big data)

Cluster, Hadoop, HDFS, MapReduce, Spark, Flink, Hive, HBase, MongoDB, Cassandra, Kafka,…

Một số ví dụ về các ứng dụng Machine Learning trong các ngành: