Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.
Covers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
Explores cache memory design, hits, misses, and eviction policies in computer systems, emphasizing spatial and temporal locality.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
