• Overfitting and Underfitting
• Regularization
• Cross-validation
• Early Stopping
• Parameter Tuning: Grid search and Randomize Search

• Simple Linear Regression
• Optimization Algorithms – Gradient Descent: Batch Gradient Descent and Stochastic Gradient Descent
• Multiple Regression
• Polynomial Regression
• Regularized Regression – Lasso and Ridge Regression
• Evaluation Metrics
• Support Vector Machine
• K-Nearest Neighbor

• Data pre-processing
• Data scaling and Normalization
• Feature scaling
• Dealing with Missing and Skewed data
• Handling text and categorical attributes
• Transformation Pipelines

• Introduction to scikit-learn
• Different types of data

• Background
• Types of Machine learning
• Machine learning pipeline
• Parametric and Non-parametric ML Algorithm

• Matplotlib
• Plotting functions in Pandas

Numpy

• Multi-dimensional Array (ndarray)
• Operations – Indexing, slicing, transpose
• Broadcasting
• File input and output

Pandas

• Data Structure: Series and DataFrame
• Indexing, Selection , Filtering, Sorting, Ranking and Summarization
• Data Aggregation
• Data loading, storage and file formats

• Python Installation
• IDE and packages - Anaconda, PyCharm and Jupyter
• IDE and packages - Anaconda, PyCharm and Jupyter
• Variables and data types
• Conditions and Loops
• Strings
• Data Structure – List, Dictionary, Tuples
• File Handling

• Basic data types and R Studio
• Control Structures and Functions
• Loop Functions
• File Operations
• Simulation and Profiling
• Data structure

• Need of learning data science
• Need of a data scientist in companies
• Who can become a data scientist
• Vs machine learning
• Vs deep learning
• Real time process of data science
  • Applications of data science
  • Technologies used in data science
  • Pre requisites to learn data science