MACHINE LEARNING WITH PYTHON

  1. Application of Machine Learning
  2. Installation of Python
  3. Introduction of Python
  1. Control Flow
    1. If-THEN-ELSE
    2. Loop with For and While
    3. Break and Continue Statement
    4. Range () Function
  2. Function
    1. User Defined
    2. System Defined
  3. Data Structures
    1. Linear Structure
    2. Qeue, Stack and Deque Interfaces
    3. List, Uset and SSet Interfaces
    4. Tuples & Ranges
    5. Strings
  4. Classes
    1. Names and Objects
    2. Scopes and Namespaces
    3. Classes and Variables
    4. Odds and Ends
  5. Exceptional Handling
    1. File Handling
    2. File Operations
    3. Dealing with Errors
    4. Importing Modules
  1. Numpy
  2. Sckit-learn
  3. Panda
  4. NLTK
  5. PILLOW
  6. Scrapy
  7. Matplotlib
  1. Data from different sources
  2. Create and Import Python Modules
  1. Data Cleansing
  2. Variable Transformation
  3. Expoloratory Data Analysis
  4. Plotting the Graphs
  1. Descriptive Statistics
  2. Hypothesis
  3. Ttest
    1. One Sample T-Test
    2. Two Sample T-test
    3. Paired T-Test
  4. Anova
    1. One Way Anova
    2. Two Way Anova
    3. Multivariate Anova
  5. Chi-Square
  6. Correlation
  7. Interpreation of Statistical Results
  1. Introduction
  2. Calculations, Equation and Assumptions
  3. Area of Application and Technique
  4. Validation Techniques
  5. Interpretation
  6. Training and Testing the Model
  1. Introduction
  2. Assumptions
  3. Area of Application and Methods
  4. Diagnostics
  5. Validation Methods
  6. Interpretation
  7. Training and Testing the Model
  1. Introduction
  2. Area of Application and Methods
  3. Hierarchal Clustering
  4. K-Means Clustering
  5. Segmentation of Clusters
  6. Interpretation of Outputs
  7. Validation
  1. Introduction
  2. Components
  3. Area of Applications
  4. Moving Average Methods
  5. Exponential Smoothing Method
  6. Holt Winter Method
  7. Box-Jenkins Method
  8. ARIMA
  9. Interpretation of Outputs
  10. Validation
  1. Introduction of Machine Learning and Components
  2. Supervised and Unsupervised Methods
  3. Sampling Techniques in Machine Learning
  4. Multi fold Validation Technique
  5. Bagging and Boosting Methods
  6. Ensemble Techniques
  7. Random Forest with Interpretation of Outputs
  8. Gradient Boosting Machines with Interpretation of Outputs
  9. XGBoost with Interpretation of Outputs
  1. Introduction
  2. Different Layers of Neural Network
  3. Multi Layers Neural Network
  4. Regression Problem
  5. Classification Problem
  6. Fine Tuning of Hyper Parameters
  7. Interpretation of Outputs
  8. Validation
  1. Introduction
  2. Regression Problem
  3. Setting of Hyper Parameters
  4. Interpretation of Outputs
  5. Validation
Course Detail
Fees:
Rs 23,000

 

Duration:
3 Months

 

Class Mode:
Classroom/Online

 

Course Content:
Python, Machine Learning, Artificial Intelligence, Pycharm, Jupyter

 

Course Benefits:
Student will able to use Python programing and know about Artificial Intelligence

 

RS 20,000

2 Months

Machine Learning with R

Machine Learning

KEY FEATURES

3 Months of Classroom Training.

 

Session have assignments and daily task.

 

Recorded Videos and presentation for reference.

Books and related data sets in form of excel and csv.

 

Interview Questions for related topic.

 

Used Case Study and Projects to understand real scenarios.

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Data
Science

Machine
Learning

Artificial
Intelligence

Python
Programming

R
Programming

Data
Visualization