Machine Learning A-Z: Hands-On Python & R In Data Science Training

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Machine Learning A-Z: Hands-On Python & R In Data Science Training Course Overview

The Machine Learning A-Z: Hands-On Python & R In Data Science Training & Certification Course aims to insight the candidates on the Data Preprocessing, Clustering: K-Means, Hierarchical Clustering, Reinforcement Learning: Upper Confidence Bound, Thompson Sampling, Dimensionality Reduction: PCA, LDA, Kernel PCA, Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost, Reinforcement Learning: Upper Confidence Bound, Thompson Sampling, Deep Learning: Artificial Neural Networks, Convolutional Neural Networks, etc. All these helps the candidates in building their career as a data science professional, who can create a strong added value to your business for sure.

After completing Machine Learning A-Z: Hands-On Python & R In Data Science Training & Certification Course, the candidates would be able to:

  • Understand how to make an accurate predictions
  • Learn how to deal with the advanced techniques like Dimensionality Reduction
  • Develop an understanding on the issues of specific topics like Reinforcement Learning, NLP and Deep Learning and how to handle it
  • Develop understanding of many of the Machine Learning models
  • Develop understanding on all the essentials such as: Data Preprocessing, Regression, Classification, Clustering, Association Rule Learning, Reinforcement Learning, Natural Language Processing, Deep Learning, Dimensionality Reduction, and Model Selection & Boosting.
Target audience
  • Individuals interested in Machine Learning and have at least high school knowledge in math
  • Any intermediate level people who has the knowledge of  the basics of machine learning, which includes the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
  • The individuals not very much compatible with the coding, but who are interested in Machine Learning and want to apply it easily on data sets.
  • The college students, who wish to start a career in Data Science
  • The data analysts, who wish to level up in Machine Learning
  • The individuals, who are planning to switch their jobs to become a data scientist
  • The individuals, who wish to create added value to their business by using powerful Machine Learning tools
Prerequisites

The candidates required to have good knowledge of high school level mathematics.

Machine Learning A-Z: Hands-On Python & R In Data Science Training Course Content

1. Data Preprocessing

  • Overview of Data Preprocessing
  • Get the dataset
  • Importing the Libraries
  • Importing the Dataset
  • Missing Data
  • Categorical Data
  • Splitting the Dataset into the Training set and Test set
  • Feature Scaling
  • How to Set Up Working Directory

2. Regression

3. Simple Linear Regression

  • How to get the dataset
  • Dataset + Business Problem Description
  • Simple Linear Regression Intuition
  • Simple Linear Regression in Python
  • Simple Linear Regression in R

4. Multiple Linear Regression

  • How to get the dataset
  • Dataset + Business Problem Description
  • Multiple Linear Regression Intuition
  • Multiple Linear Regression in Python
  • Multiple Linear Regression in Python - Backward Elimination - Preparation
  • Multiple Linear Regression in R

5. Polynomial Regression

  • Polynomial Regression Intuition
  • How to get the dataset
  • Polynomial Regression in Python
  • Python Regression Template
  • Polynomial Regression in R
  • R Regression Template

6. Support Vector Regression (SVR)

  • How to get the dataset
  • SVR in Python
  • SVR in R

7. Decision Tree Regression

  • Decision Tree Regression Intuition
  • How to get the dataset
  • Decision Tree Regression in Python
  • Decision Tree Regression in R

8. Random Forest Regression

  • Random Forest Regression Intuition
  • How to get the dataset
  • Random Forest Regression in Python
  • Random Forest Regression in R

9. Evaluating Regression Models Performance

  • R-Squared Intuition
  • Adjusted R-Squared Intuition
  • Interpreting Linear Regression Coefficients

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