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    Course content

    About Machine Learning Course using Python (CMLP) in Hughes Springs

  • Installing Python and Anaconda
  • This is a Milestone!
  • Opening Jupyter Notebook
  • Introduction to Jupyter
  • Arithmetic operators in Python: Python Basics
  • Quick coding exercise on arithmetic operators
  • Strings in Python: Python Basics
  • Quick coding exercise on String operations
  • Lists, Tuples and Directories: Python Basics
  • Quick coding exercise on Tuples
  • Working with Numpy Library of Python
  • Quick coding exercise on NumPy Library
  • Working with Pandas Library of Python
  • Quick coding exercise on Pandas Library
  • Working with Seaborn Library of Python
  • Python file for additional practice

  • Types of Data
  • Types of Statistics
  • Describing data Graphically
  • Measures of Centers
  • Measures of Dispersion

  • Introduction to Machine Learning
  • Building a Machine Learning Model

  • Gathering Business Knowledge
  • Data Exploration
  • The Dataset and the Data Dictionary
  • Importing Data in Python
  • Univariate analysis and EDD
  • EDD in Python
  • Outlier Treatment
  • Outlier Treatment in Python
  • Missing Value Imputation
  • Missing Value Imputation in Python
  • Seasonality in Data
  • Bi-variate analysis and Variable transformation
  • Variable transformation and deletion in Python
  • Non-usable variables
  • Dummy variable creation: Handling qualitative data
  • Dummy variable creation in Python
  • Correlation Analysis
  • Correlation Analysis in Python

  • The Problem Statement
  • Basic Equations and Ordinary Least Squares (OLS) method
  • Assessing accuracy of predicted coefficients
  • Assessing Model Accuracy: RSE and R squared
  • Simple Linear Regression in Python
  • Multiple Linear Regression
  • The F - statistic
  • Interpreting results of Categorical variables
  • Multiple Linear Regression in Python
  • Test-train split
  • Bias Variance trade-off
  • Test train split in Python
  • Regression models other than OLS
  • Subset selection techniques
  • Shrinkage methods: Ridge and Lasso
  • Ridge regression and Lasso in Python
  • Heteroscedasticity

  • Three classification models and Data set
  • Importing the data into Python
  • The problem statements
  • Why can't we use Linear Regression?

  • Logistic Regression
  • Training a Simple Logistic Model in Python
  • Result of Simple Logistic Regression
  • Logistic with multiple predictors
  • Training multiple predictor Logistic model in Python
  • Confusion Matrix
  • Creating Confusion Matrix in Python
  • Evaluating performance of model
  • Evaluating model performance in Python

  • Linear Discriminant Analysis
  • LDA in Python

  • Test-Train Split
  • Test-Train Split in Python
  • K-Nearest Neighbors classifier
  • K-Nearest Neighbors in Python

  • Understanding the results of classification models
  • Summary of the three models

  • Introduction to Decision Trees
  • Basics of Decision Trees
  • Understanding a Regression Tree
  • The stopping criteria for controlling tree growth
  • Importing the Data set into Python
  • Missing value treatment in Python
  • Dummy Variable Creation in Python
  • Dependent- Independent Data split in Python
  • Test-Train split in Python
  • Creating Decision tree in Python
  • Evaluating model performance in Python
  • Plotting decision tree in Python
  • Pruning a tree
  • Pruning a tree in Python

  • Classification Tree
  • The Data Set for Classification Problem
  • Classification Tree in Python: Preprocessing
  • Classification Tree in Python: Training
  • Advantages and Disadvantages of Decision Trees

  • Introduction to SVMs
  • The Concept of a Hyperplane
  • Maximum Margin Classifier
  • Limitations of Maximum Margin Classifier

  • Support Vector Classifiers
  • Limitations of Support Vector Classifiers

  • Regression and Classification Models
  • Importing and Preprocessing Data in Python
  • Standardizing the Data
  • SVM Based Regression Model in Python
  • Classification Model - Preprocessing
  • Classification Model - Standardizing the Data
  • SVM Based Classification Model
  • Hyperparameter Tuning
  • Polynomial Kernel with Hyperparameter Tuning
  • Radial Kernel with Hyperparameter Tuning

  • Introduction
  • Time Series Forecasting - Use Cases
  • Forecasting Model Creation - Steps
  • Forecasting Model Creation - Steps 1 (Goal)
  • Time Series - Basic Notations

  • Data Loading in Python
  • Time Series - Visualization Basics
  • Time Series - Visualization in Python
  • Time Series - Feature Engineering Basics
  • Time Series - Feature Engineering in Python
  • Time Series - Upsampling and Downsampling
  • Time Series - Upsampling and Downsampling in Python
  • Time Series - Power Transformation
  • Moving Average
  • Exponential Smoothing

  • White Noise
  • Random Walk
  • Decomposing Time Series in Python
  • Differencing
  • Differencing in Python

  • Test Train Split in Python
  • Naive (Persistence) model in Python
  • Auto Regression Model - Basics
  • Auto Regression Model creation in Python
  • Auto Regression with Walk Forward validation in Python
  • Moving Average model - Basics
  • Moving Average model in Python

  • ACF and PACF
  • ARIMA model - Basics
  • ARIMA model in Python
  • ARIMA model with Walk Forward Validation in Python

  • SARIMA model
  • SARIMA model in Python
  • Stationary time Series
  • Comprehensive Interview Preparation Questions
  • The final milestone!
    • Why Should You Learn Machine Learning Course using Python (CMLP) in Hughes Springs Training?

    The annual salary of an Machine Learning Course using Python (CMLP) in Hughes Springs is $125k.

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    Track Week Days Course Duration Fast Track
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    Course Duration 7 Weekends 3 Hrs. Per Day Online
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    Students Reviews

    Read genuine student reviews on ZebLearn’s expert-led SAP training and foreign language courses. Hear success stories from learners who gained certifications, skills, and career growth!

    Aarti Kapoor
    Data Scientist
    The Machine Learning Course using Python from Zeblearnindia was exceptional. The course provided a comprehensive understanding of machine learning algorithms and practical skills in Python. The hands-on projects and real-world applications were particularly valuable. This training has greatly enhanced my capabilities as a data scientist.
    Ravi Reddy
    Machine Learning Engineer
    Zeblearnindia’s Machine Learning Course was fantastic. The course covered all essential aspects of machine learning using Python, from data preprocessing to model deployment. The practical exercises and in-depth projects were very helpful. This training has significantly improved my skills in machine learning and Python programming.
    Priya Sinha
    Research Analyst
    The Machine Learning Course using Python at Zeblearnindia was highly informative. The course provided a solid foundation in machine learning concepts and Python tools. The focus on real-world projects and model evaluation techniques was especially useful. This course has been crucial in advancing my career as a research analyst.
    Sameer Patel
    AI Specialist
    Completing the Machine Learning Course with Zeblearnindia was a great experience. The course offered a detailed exploration of machine learning algorithms and practical Python applications. The hands-on projects and deep learning basics were particularly beneficial. This training has enhanced my skills and knowledge as an AI specialist.
    Neelam Sharma
    Data Analyst
    The Machine Learning Course using Python from Zeblearnindia was outstanding. The course provided a thorough understanding of machine learning techniques and Python libraries. The real-world projects and practical exercises helped me apply the concepts effectively. This course has been essential in improving my role as a data analyst.

    Machine Learning Course using Python (CMLP) in Hughes Springs - Flexible batches for you

    Date Type Schedule Time
    SOLD OUT 16 March 2025 Weekend SAT - SUN (08 Week) 18:00 To 20:00
    Filling Img 21 March 2025 Weekday MON - FRI (08 Week) 08:00 To 10:00
    26 March 2025 Weekend MON - FRI (08 Week) 10:00 To 00:00

    Price  1,20,000

    Now  95,000

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