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C-Cube
Centre of Computer Education
Course : Data Science & Machine Learning
Data Science with Python
Full Course for Beginners
- Python Introduction
- Types
- Expressions and Variables
- String Operations
- Lists and Tuples
- Sets
- Dictionaries
- Conditions and Branching
- Loops
- Functions
- Objects and Classes
- Array shapes and axes
- Arithmetic Operations
- Conditional Logic
- Common Mathematical & Statistical Functions
- Indexing & Slicing
- Scalars & Vectors
- Dot Product of Two Vectors
- Matrix
- Matrix Operations
- Transpose of Matrix
- Importance of Statistics with Respect to Data Science
- Levels of Measurement
- Measures of Central Tendency
- Measure of Dispersion
- Measures of Shape
- Covriance & Correlation
- Introduction to Pandas
- Pandas Series
- Pandas Dataframes
- Common Functions in Pandas
- Pandas Functions of Data Statistical Function, Window Function
- Categorical Data
- Working with Text Data
- Iteration
- Sorting
- Plotting with Pandas
- Data Importing and Exporting
- Data Cleaning and Preprocessing
- Data Manipulation
- Exploratory Data Analysis (EDA)
- Handling Time Series Data
- Data Export
- Handling Missing Data
- Removing Duplicates
- Data Type Conversion
- Renaming Columns
- Filtering and Subsetting Data
- String Manipulation
- Handling Outliers
- Merging and Joining DataFrames
- Pivoting and Reshaping Data
- Grouping and Aggregation
- Matplotlib Python Intro
- MatplotlibPyplot
- Matplotlib Plotting
- Matplotlib Markers
- Matplotlib Line
- Matplotlib Labels
- Matplotlib Grid
- Matplotlib Subplots
- Matplotlib Scatter
- Matplotlib Bars
- Matplotlib Histograms
- Matplotlib Pie Charts
- Dynamic Visualizations, Scipy
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Machine Learning
with Python
Full Course for Beginners & Advance Level
- Machine Learning Introduction.
- Types of Machine Learning.
- Introduction of Packages used in Machine Learning.
- Machine Learning vs Statistical Modelling
- Supervised vs Unsupervised Learning
- Supervised Learning Classification
- Unsupervised Learning
- K-Nearest Neighbors
- Decision Trees
- Random Forests
- Reliability of Random Forests
- Advantages & Disadvantages of Decision Trees
- Regression Algorithms
- Model Evaluation
- Model Evaluation: Overfitting & Underfitting
- Understanding Different Evaluation Models
- K-Means Clustering plus Advantages & Disadvantages
- Hierarchical Clustering plus Advantages & Disadvantages
- Measuring the Distances Between Clusters – Single Linkage Clustering
- Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
- Density-Based Clustering
- Dimensionality Reduction: Feature Extraction & Selection
- Collaborative Filtering & Its Challenges