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    Introduction to Machine Learning Algorithms and their Implementation in Python

    BY
    Skill Lync

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    Mode

    Online

    Duration

    12 Weeks

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    The fees for the course Introduction to Machine Learning Algorithms and their Implementation in Python is -

    HeadAmount
    Programme feesRs. 40,000
    certificate availability

    Yes

    certificate providing authority

    Skill Lync

    The syllabus

    Week 1- Introduction to Data Science and Programming Languages (Tools) for Data Science

    • Data Science and Big Data: Introduction 
    • Importance of Data Science and Big Data
    • Introduction to Different Programming Languages (Tools) for Data Science

    Week 2- Basics of Programming

    • Variables
    • Operators
    • Data Types
    • Data Structure
    • Control Structure in Python
    • Function File in Python

    Week 3- Essential Python Libraries

    • NumPy
    • SciPy
    • Pandas
    • Matplotlib
    • Seaborn

    Week 4- Introduction to Machine Learning Cross-Validation, Bias-Variance Tradeoff

    • Basics of Machine Learning
    • Classification
    • Fitting of Model with Cross-Validation
    • Bias Variance Tradeoff.

    Week 5- Evaluation Metrics

    • Evaluation Metrics for Model Validation

    Week 6- Importing Data and Hands on Imported Data

    • EDA
    • Correlation
    • Feature Extraction
    • Hyper Parameters

    Week 7- Univariate and Multivariate Linear Regression

    • Univariate Linear Regression
    • Multivariate Linear Regression
    • Implemented in Python

    Week 8- Principal Component Analysis

    • Eigen Values
    • Eigen Vectors
    • Singular Value Decomposition
    • PCA

    Week 9- Logistic Regression and k-nearest Neighbor

    • Explanation and Implementation of Logistic Regression and k-nearest Neighbor in Python

    Week 10- Decision Tree and Random Forest

    • Decision Tree
    • Random Forest

    Week 11- K-Mean and Hierarchical Clustering

    • K-Mean
    • Hierarchical Clustering

    Week 12- Neural Network

    • Logistic Regression with Neural Network Mindset

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