The course curriculum Training in Machine Learning:

Introduction Topics

  • Machine Learning.
  • Data Mining.
  • Deep Learning.
  • Artificial Intelligence
  • Descriptive Analysis.
  • Predictive Analysis.

Python

  • Basics Python
  • Types of Variables
  • Numbers
  • Strings
  • Lists
  • Dictionaries
  • Tuples
  • Statements
  • Looping
  • Function
  • Anaconda Distribution
  • Working Framework Jupyter Notebook

NumPy.

  • NdArray
    • ndim, shape, size, dtype, itemsize, data
    • Array Creation
    • Printing Arrays
    • Basic Operations
    • Universal Functions
    • Indexing, Slicing and Iterating
  • Shape Manipulation
    • Changing the shape of an array
    • Stacking together different arrays
    • Splitting one array into several smaller ones
  • Copies and Views
    • No Copy at All
    • View or Shallow Copy
    • Deep Copy
  • Fancy indexing and index tricks
    • Indexing with Arrays of Indices
    • Indexing with Boolean Arrays
    • The ix_() function
  • Linear Algebra
    • Simple Array Operations

Pandas

  • Pandas Basics
    • Object Creation
    • Viewing Data
    • Selection
    • Missing Data
    • Operations
    • Merge
    • Grouping
    • Reshaping
    • Time Series
    • Plotting
    • Getting Data In/Out
  • Intro to Data Structures
    • Series
    • DataFrame
    • Panel
    • Deprecate Panel

Matplotlib

  • Introductory
  • Intermediate
  • Advanced
  • Colors
  • Text

Data Preprocessing and Data Analysis

  • Data Cleaning or Data cleansing.
  • Data Integration.
  • Data Transformation.
  • Data Reduction.
  • Data Discretisation.
  • Data Visualisation.
  • Sql Queries.

Statistics

  • Descriptive Statistics.
    • Mean, Median, Mode, Variance etc
  • Inferential Statistics.
    • Linear Regression.
    • Binomial Distribution.
    • Normal Distribution.
    • Chi-Squared Test
    • Probability.
    • Permutation and Combination.
    • Least Square etc

Machine Learning

  • Supervised Learning.
    • Classification Techniques
      • Regression Techniques
        • Dependent variable
        • Independent variable
        • Population
        • Random sampling
        • Gradient Descent.
        • Cost Function.
        • Hypothesis function
        • Least squares
        • Regularization
        • Correlation.
        • Training Data and Test Data.
        • Cross Validation.
        • Type I and type II error.
        • Interpolation and Extrapolation.
        • False Positive and False Negative.
        • Bias and Variance.
        • File Format.
    • Linear Regression With One Variable.
    • Linear Regression With Multiple Variable.
    • Polynomial Regression.
    • Logistic Regression.
    • Decision Tree Regression.
    • Random Forest Regression
    • Support Vector Machine (SVM).
      • Hyper Planes
      • Support Vectors
        • Small margin
        • Large margin
    • Time Series Forecasting.
      • Trends
      • Linear Trend and Non Linear Trend
      • Seasonal Trend
      • Cyclical Trend
      • Irregular Trend
      • Autoregressive ( AR )
      • Moving Average ( MA )
      • Autoregressive Moving Average ( ARMA )
      • Autoregressive Integrated Moving Average ( ARIMA )
    • Naive Bayes.
    • K Nearest Neighbours
  • Unsupervised Learning
    • Clustering.
    • K means Clustering
    • Hierarchical Clustering.
    • Mean Shift Clustering.
  • Reinforcement Learning ( AI )
    • Neural Network.
    • Convolution Neural Network.
      • Image recognition
      • Video recognition
    • Artificial Neural Network.
      • Single Layer Network.
      • Two Layer Network.
      • Feed Forward Layer Network.
      • Fully Connected Layer Network
    • Recommendation System.
      • Collaborative Filtering
      • Content Based Filtering.

Source Code

  • Source Code