Topics for this course

Total Learning topics : 174    Chapter : 20

Machine Learning-Introduction

·         Introduction of Machine Learning

·         Evolution of Machine Learning

·         Application of Machine Learning

Machine Learning in Different Sectors

·         Introduction

·         AI & ML for Products or Services

·         How Google Uses Artificial Intelligence and Data Science

·         How LinkedIn Uses Artificial Intelligence and Data Science

·         How Amazon Uses Artificial Intelligence and Data Science

·         Netflix: Using Artificial Intelligence and Data Science to Drive Engagement

·         Media and Entertainment Industry

·         Education Industry

·         Healthcare Industry

·         Government

·         Weather Forecasting

·         Key Takeaways

Machine Learning-Fundamental

·         Difference between Traditional Programming and ML Programming

·         Requirements for Machine Learning Practical Implementation

·         Required software and tools for Machine Learning implementations.

·         Setup Anaconda

·         Installation of PyCharm or Spyder or Jupyter

·         Configure PyCharm/Spyder/Jupyter with Anaconda

Python Programming Foundation for Data Science

·         Introduction

·         Variables

·         Data Types with Python

·         Assisted Practice: Data Types in Python

·         Keywords and Identifiers

·         Expressions

·         Basic Operators

·         Operators in Python

·         Functions

·         Search for a Specific Element from a Sorted List

·         String Operations

·         String Operations in Python

·         Tuples

·         Tuples in Python

·         Lists

·         Lists in Python

·         Sets

·         Sets in Python

·         Dictionaries

·         Dictionary in Python

·         Dictionary and its Operations

·         Conditions and Branching

·         While Loop

·         For Loop

·         Break and Continue Statements

File handling and Package handling using Python.

·         Learning Objectives

·         File Handling

·         File Opening and Closing

·         Reading and Writing Files

·         Directories in File Handling

·         Assisted Practice: File Handling

·         Modules and Packages

·         Assisted Practice: Package Handling

Mathematical Computing using NumPy

·         Learning objectives

·         Introduction of NumPy

·         Create and Print Numpy Arrays

·         Operations

·         Executing Basic Operations in Numpy Array

·         Performing Operations Using Numpy Array

·         Demonstrate the Use of Copy and Use

·         Manipulate the Shape of an Array

Data Manipulation with Pandas

·         Learning Objectives

·         Introduction to Pandas

·         Data Structures

·         Create Pandas Series

·         DataFrame

·         Create Pandas DataFrames

·         Missing Values

·         Handle Missing Values

·         Various Data Operations

·         Data Operations in Pandas DataFrame

Data visualization with Python

·         Learning objectives

·         Data Visualization

·         Considerations of Data Visualization

·         Factors of Data Visualization

·         Python Libraries

·         Create Your First Plot Using Matplotlib

·         Line Properties

·         Multiple Plots and Subplots

·         Create a Plot with Annotation

·         Create Multiple Subplots Using plt.subplots

·         Types of plots

·         Create a Stacked Histogram

·         Create a Scatter Plot

·         Create a Pie Chart

·         Create a Bar Chart

·         Create Box Plots

·         Analysing Variables Individually

·         Key Takeaways

Steps of Machine Learning Implementations

·         Types of Machine Learning

·         Labelled Data and Unlabelled Data

·         Steps of Machine Learning

·         Concept of Collecting the historic training Data for ML

·         Concept of Pre-process data for Machine Learning

·         Concept of Train the ML model

·         Concept of Test the ML Algorithm

·         Concept of using the ML Algorithm

Data Collection for Machine Learning

·         Introduction

·         Types of Data collection- Offline Data and Online Data

·         Practical implementations of Reading the offline dataset using Numpy

·         Practical implementations of Reading the online dataset using Numpy

·         Practical implementations of Reading the offline dataset using Pandas

·         Practical implementations of Reading the online iris dataset using Pandas

Concept of Supervise & Unsupervised Machine Learning

·         Introduction

·         Types of Machine Learning

·         Labelled Data and Unlabeled Data

·         Concept of Supervised Machine Learning

·         Concept of Unsupervised Machine Learning

·         Regression and Classification

·         Linear Regression and Logistic Regression

Data Plotting for Machine Learning 

·         Introduction

·         Concept of Univariate plots

·         Univariate Histogram Plots.

·         Univariate Density Plots.

·         Univariate Box and Whisker Plots.

·         Concept of Multivariate plots

·         Correlation Matrix Plot

·         Scatter Matrix Plot

Prepare Data for ML using Data Transformation Methods

·         Introduction

·         Need for Data Pre-processing

·         Data Transforms Steps

·         Types of Data Transformation Methods

·         Rescale Data

·         Standardize Data

·         Normalize Data

·         Binarize Data

Practical implementation of Supervised ML Algorithm

·         Introduction

·         Implementation Foundation of Supervised Machine Learning Algorithms

·         Regression and Classification

·         Linear Regression and Logistic Regression

·         Practical implementations of Supervised ML Algorithms- Linear Regression

·         Practical implementations of Supervised ML Algorithms- Logistic Regression

·          Concept of Sigmoid Function

·         k-NN Algorithm

·         Naive Bayes Classifiers

·         Decision trees etc.

·         Concept of Support vector machines

·         Introduction to concepts of forecasting 

Data Resampling Methods for Evaluation of ML Models

·         Introduction

·         Evaluate Machine Learning Algorithms

·         Split into Train and Test Sets

·         K-fold Cross Validation

·         Leave One Out Cross Validation

·         Repeated Random Test-Train Splits

·         What Techniques to Use When

Machine Learning Algorithm Performance Evaluation Metrics

·         Introduction

·         Algorithm Evaluation Metrics

·         Logistic Regression Algorithm Performance Evaluation Metrics

·         Classification Accuracy (Default).

·         Logarithmic Loss.

·         Area Under ROC Curve (AUC).

·         Confusion Matrix.

·         Classification Report.

·         Linear Regression Algorithm Performance Evaluation Metrics

·         Mean Absolute Error.

·         Mean Squared Error.

·         R2 Error

Practical implementation of Unsupervised Machine Learning 

·         Introduction

·         Concepts and Steps of Unsupervised Machine Learning Algorithm 

·         Concept of Clustering,

·         Practical implementations of Machine Learning Unsupervised Algorithms

·         K-Means Clustering.

Spot-Check Machine Learning Algorithms

·         Concept of Algorithm Spot-Checking

·         Algorithms Overview

·         Linear Machine Learning Algorithms Spot-check

·         Nonlinear Machine Learning Algorithms Spot-check

Save and Load Machine Learning Models

·         Introduction

·         Finalize Your Model with pickle.

·         Finalize Your Model with Joblib

Conclusion and Future of Machine Learning

·         Summary of machine learning concepts and techniques

·         Machine learning trends and future developments

·         Machine learning and society

·         Machine learning ethics and safety.