SAS Training

 

MACHINE LEARNING

 

 

INTRODUCTION TOPICS

      1. Data Science
      2. Machine Learning.
      3. Data Mining.
      4. Deep Learning.
      5. Artificial Intelligence.
      6. Descriptive Analysis.
      7. Predictive Analysis.
      8. Python

 

 

PYTHON BASICS REQUIRED FOR DATA SCIENCE AND MACHINE LEARNING

      1. Basics Python.
          1. Types of Variables
              1. Numbers
              2. Strings
              3. Lists
              4. Dictionaries
              5. Tuples
          2. Statements
          3. Looping
          4. Function
          5. Database Connectivity in Python
      2. Installation of Anaconda Distribution
      3. Working Framework Jupyter Notebook

 

 

PYTHON LIBRARIES

      1. Introduction to Libraries Required for Data Science
          1. NumPy
          2. Pandas
          3. MatplotLib
          4. SciPy

 

 

NUMPY

      1. NdArray In NumPy
                  1. ndim, shape, size, dtype, itemsize, data
                  2. Array Creation
                  3. Printing Arrays
                  4. Basic Operations
                  5. Universal Functions
                  6. Indexing, Slicing and Iterating

 

      1. Shape Manipulation Using NumPy
                  1. Changing the shape of an array
                  2. Stacking together different arrays
                  3. Splitting one array into several smaller ones
      1. Copies and Views In NumPy
                  1. No Copy at All
                  2. View or Shallow Copy
                  3. Deep Copy

 

      1. Fancy indexing and index tricks
                  1. Indexing with Arrays of Indices
                  2. Indexing with Boolean Arrays
                  3. The ix_() function
      1. Linear Algebra In NumPy
                  1. Simple Array Operations

 

      1. Tricks and Tips
                  1. Automatic Reshaping
                  2. Vector Stacking

 

 

PANDAS

      1. Pandas Basics
          1. Object Creation
          2. Viewing Data
          3. Selection
          4. Missing Data
          5. Operations
          6. Merge
          7. Grouping
          8. Reshaping

 

      1. Essential Basic Functionality
          1. Head and Tail
          2. Attributes and Underlying Data
          3. Accelerated operations
          4. Flexible binary operations
          5. Descriptive statistics
          6. Function application
          7. Reindexing and altering labels
          8. Iteration
          9. .dt accessor
          10. Vectorized string methods
          11. Sorting
          12. Copying
          13. dtypes
          14. Selecting columns based on dtype
      1. Intro to Data Structures
          1. Series
          2. DataFrame
          3. Panel

 

 

MATPLOTLIB

      1. Introduction of Matplot Library
      2. Intermediate
      3. Advanced
      4. Colors
      5. Text
      6. Toolkits

 

 

SCIPY

      1. Introduction Of SciPy Library
      2. Basic functions
      3. Special functions
      4. Integration
      5. Optimization
      6. Interpolation
      7. Signal Processing
      8. Linear Algebra
      9. Spatial data structures and algorithms
      10. Statistics

 

 

TYPES OF MACHINE LEARNING

  1. Supervised Learning
  2. Unsupervised Learning
  3. Reinforcement Learning

 

 

SUPERVISED LEARNING

 

    1. Factors in Machine Learning
      1. What is Dependent variable ?
      2. What is Independent variable ?
      3. What is Sample And Population ?
      4. Least squares
      5. Regularization
      6. Correlation.
      7. Training Data and Test Data.
      8. Cross Validation.
      9. Type I and type II error.
      10. Interpolation and Extrapolation.
      11. False Positive and False Negative.
      12. Bias and Variance.
      13. File Format.
      14. Outliers
      15. Underfitting and Overfitting
      16. Dimensionality Reduction
    1. Regression Algorithms.
                  1. Linear Regression With One Variable.
                  2. Gradient Descent.
                  3. Cost Function.
                  4. Hypothesis function
                  5. Linear Regression With Multiple Variable.
                  6. Polynomial Regression.
                  7. Logistic Regression.
                  8. Decision Tree Regression.
                  9. Random Forest Regression.

 

    1. Classification Algorithms.
                  1. Support Vector Machine (SVM).
      1. Hyper Planes
      2. Support Vectors
      3. Small margin
      4. Large margin

 

                  1. Time Series Forecasting.
      1. Trends
      2. Linear Trend and Non Linear Trend
      3. Seasonal Trend
      4. Cyclical Trend
      5. Irregular Trend
      6. Autoregressive ( AR )
      7. Moving Average ( MA )
      8. Autoregressive Moving Average ( ARMA )
      9. Autoregressive Integrated Moving Average ( ARIMA )
                  1. Naive Bayes.
                  2. K Nearest Neighbours.

 

 

UNSUPERVISED LEARNING

          1. What is Clustering ?
          2. Why Clustering ?
          3. Applications of Clustering
          4. Types of Clustering Algorithms
                  1. K means Clustering.
                  2. Hierarchical Clustering.
                  3. Mean Shift Clustering.

 

 

REINFORCEMENT LEARNING ( INTRODUCTION )

      1. Neural Network.
      2. Convolution Neural Network.
      3. Artificial Neural Network.
      4. Natural Language Processing. ( NLTK )
      5. Recommendation System.