Machine Learning
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4.3
Machine learning is a subfield of artificial intelligence (AI). The goal of machine learning generally is to understand the structure of data and fit that data into models that can be understood and utilized by people.
Machine learning is a continuously developing field. Because of this, there are some considerations to keep in mind as you work with machine learning methodologies, or analyze the impact of machine learning processes.
Machine learning algorithms instead allow for computers to train on data inputs and use statistical analysis in order to output values that fall within a specific range. Because of this, machine learning facilitates computers in building models from sample data in order to automate decision-making processes based on data inputs.
Topics Include:
- Learn how to solve real life problem using the Machine learning techniques
- Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc.
- How to do basic statistical operations and run ML models in Python
- Be a comfortable front-end developer
- Be proficient with databases and server-side languages
- Bid for projects on freelance websites
Curriculum for the Course
- 136 Lectures
- 16 Hours
- Introduction 04:12 preview
- Installing Python and Anaconda 03:04 preview
- This is a milestone! 03:31 preview
- Opening Jupyter Notebook 09:06 preview
- Introduction to Jupyter 13:26 preview
- Arithmetic operators in Python Python Basics 04:28 preview
- Strings in Python Python Basics 19:07 preview
- Lists, Tuples and Directories Python Basics 18:41 preview
- Working with Numpy Library of Python 11:54 preview
- Working with Pandas Library of Python 09:15 preview
- Working with Seaborn Library of Python 08:57 preview
- Installing R and R studio 05:52 preview
- Basics R and R studio 10:47 preview
- Packages in R 10:52 preview
- Inputting data part 1 Inbuilt datasets of R 04:21 preview
- Inputting data part 2 Manual data entry 03:11 preview
- Inputting data part 3 Importing from CSV or Text files 06:49 preview
- Creating Barplots in R 13:43 preview
- Creating Histograms in R 06:01 preview
- Gathering Business Knowledge 03:26 preview
- Data Exploration 03:19 preview
- The Dataset and the Data Dictionary 07:31 preview
- Importing Data in Python 06:04 preview
- Importing the dataset into R 03:00 preview
- Univariate analysis and EDD 03:34 preview
- EDD in Python 12:11 preview
- EDD in R 12:43 preview
- Outlier Treatment 04:15 preview
- Outlier Treatment in Python 14:18 preview
- Outlier Treatment in R 04:49 preview
- Missing Value Imputation in Python 04:57 preview
- Missing Value imputation in R 03:49 preview
- Seasonality in Data 03:35 preview
- Bi-variate analysis and Variable transformation 16:14 preview
- Variable transformation and deletion in Python 09:21 preview
- Variable transformation in R 09:37 preview
- Non-usable variables 04:44 preview
- Dummy variable creation Handling qualitative data 04:50 preview
- Dummy variable creation in Python 05:45 preview
- Dummy variable creation in R 05:01 preview
- Correlation Analysis 10:05 preview
- Correlation Analysis in Python 07:07 preview
- Correlation Matrix in R 08:09 preview
- The Problem Statement 01:25 preview
- Basic Equations and Ordinary Least Squares (OLS) method 08:13 preview
- Assessing accuracy of predicted coefficients 14:40 preview
- Assessing Model Accuracy RSE and R squared 07:19 preview
- Simple Linear Regression in Python 14:07 preview
- Simple Linear Regression in R 07:40 preview
- Multiple Linear Regression 04:57 preview
- The F - statistic 08:22 preview
- Interpreting results of Categorical variables 05:04 preview
- Multiple Linear Regression in Python 14:13 preview
- Multiple Linear Regression in R 07:50 preview
- Test-train split 09:32 preview
- Bias Variance trade-off 06:01 preview
- Test train split in Python 10:19 preview
- Test-Train Split in R 08:44 preview
- Regression models other than OLS 04:18 preview
- Subset selection techniques 11:34 preview
- Subset selection in R 07:38 preview
- Shrinkage methods Ridge and Lasso 07:14 preview
- Ridge regression and Lasso in Python 23:50 preview
- Ridge regression and Lasso in R 12:52 preview
- Heteroscedasticity 02:30 preview
- The Data and the Data Dictionary 08:14 preview
- Data Import in Python 04:56 preview
- Importing the dataset into R 03:00 preview
- EDD in Python 18:01 preview
- EDD in R 11:26 preview
- Outlier treatment in Python 09:53 preview
- Outlier Treatment in R 04:49 preview
- Missing Value Imputation in Python 04:49 preview
- Missing Value imputation in R 03:49 preview
- Variable transformation and Deletion in Python 04:55 preview
- Variable transformation in R 06:28 preview
- Dummy variable creation in Python 05:45 preview
- Dummy variable creation in R 05:19 preview
- Logistic Regression 07:54 preview
- Training a Simple Logistic Model in Python 12:25 preview
- Training a Simple Logistic Model in R 03:34 preview
- Result of Simple Logistic Regression 05:11 preview
- Logistic with multiple predictors 02:22 preview
- Training multiple predictor Logistic model in Python 06:05 preview
- Training multiple predictor Logistic model in R 01:48 preview
- Confusion Matrix 03:47 preview
- Creating Confusion Matrix in Python 09:55 preview
- Evaluating performance of model 07:40 preview
- Evaluating model performance in Python 02:22 preview
- Predicting probabilities, assigning classes and making Confusion Matrix in R 06:23 preview
- Basics of Decision Trees 10:10 preview
- Understanding a Regression Tree 10:17 preview
- The stopping criteria for controlling tree growth 03:15 preview
- The Data set for this part 02:59 preview
- Importing the Data set into Python 05:40 preview
- Importing the Data set into R 06:26 preview
- Missing value treatment in Python 03:38 preview
- Dummy Variable creation in Python 04:58 preview
- Dependent- Independent Data split in Python 04:02 preview
- Test-Train split in Python 06:04 preview
- Splitting Data into Test and Train Set in R 05:30 preview
- Creating Decision tree in Python 03:47 preview
- Building a Regression Tree in R 14:18 preview
- Evaluating model performance in Python 04:10 preview
- Plotting decision tree in Python 04:58 preview
- Pruning a tree 04:16 preview
- Pruning a tree in Python 10:37 preview
- Pruning a tree in R 09:18 preview
- Classification tree 06:06 preview
- The Data set for Classification problem 01:38 preview
- Classification tree in Python : Preprocessing 08:25 preview
- Classification tree in Python : Training 13:13 preview
- Building a classification Tree in R 08:59 preview
- Advantages and Disadvantages of Decision Trees 01:34 preview
