What you would learn in Machine Learning in Python course?
This course will assist you in building Machine Learning skills for solving problems that arise in the modern digital age. Machine Learning combines computer science and statistics to study the raw data in real-time, discover trends, and predict. Participants will learn about the most important methods and tools for building Machine Learning solutions for businesses. It is not necessary to have any technical expertise to master this technique.
What do you need to know:
What is Machine Learning does and its importance
Learn about the role of Machine Learning
What is the definition of Statistics?
Learn about the different kinds of Descriptive Statistics
Discuss the significance of Probability and the importance it plays in
Define how Probability Process happens
Define the objectives of the Data Gathering Step
Learn about the various concepts of data Preparation and Exploratory Analysis Step
Define what supervised learning is.
Define Key Differences Between Supervised or Unsupervised Learning
Find out the distinction between the three categories of machine learning. Three Categories of Machine Learning
Examine the use of Two Categories of Supervised Learning
Define the significance of Linear Regression
Learn about the different kinds of Logistic Regression.
Learn about the definition of an Integrated Development Environment and its significance
Learn the reasons why developers utilize the Integrated Development Environment
Learn the essential aspects of how to perform Addition operations and then close the Jupyter Notebook
Apply and utilize Different Operations in Python
Discussion Arithmetic Operation in Python
Find out the various types of built-in-data types in Python
Learn the essential considerations of Dictionaries-Built-in Data types
Discuss the use of Operations in Python and the significance of it
Know the significance of Logical Operators
Define the different kinds of Controlled Statements.
Learn to design and program to determine the highest number
Contents and an Overview
The course will begin by introducing an overview of the History of Machine Learning; The difference between Traditional Programming in addition to Machine Learning; What does Machine Learning do; the Definition of Machine Learning; Apply Apple Sorting Examples of Experiments; Role in Machine Learning; Machine Learning Key Terms; The Basic Terms of Statistics Descriptive Statistics-Types of Statistics and types of descriptive Statistics What is inferential Statistics Analyzing and its different types Probability and real-world examples of Probability as a process; Perspectives on Probability The Basic Theory of Probability.
Then you will learn about Defining Objectives and Data Gathering Step; Data Preparation and Data Exploratory Analysis Step; Building a Machine Learning Model and Model Evaluation; Prediction Step in the Machine Learning Process; How can a machine solve a problem-Lecture overview; What is Supervised Learning; What is Unsupervised Learning; What is Reinforced Learning; Key Differences Between Supervised,Unsupervised and Reinforced Learning; Three Categories of Machine Learning; What is Regression, Classification and Clustering; Two Categories of Supervised Learning; Category of Unsupervised Learning; Comparison of Regression , Classification and Clustering; What is Linear Regression; Advantages and Disadvantages of Linear Regression; Limitations of Linear Regression; What is Logistic Regression; Comparison of Linear Regression and Logistic Regression; Types of Logistic Regression; Advantages and Disadvantages of Logistic Regression; Limitations of Logistic Regression; What is Decision tree and its importance in Machine learning; Advantages and Disadvantages of Decision Tree.
We will also cover What is Integrated Development Environment; Parts of Integrated Development Environment; Why Developers Use Integrated Development Environment; Which IDE is used for Machine Learning; What are Open Source IDE; What is Python; Best IDE for Machine Learning along with Python; Anaconda Distribution Platform and Jupyter IDE; Three Important Tabs in Jupyter; Creating new Folder and Notebook in Jupyter; Creating Three Variables in Notebook; How to Check Available Variables in Notebook; How to Perform Addition operation and Close Jupyter Notebook; How to Avoid Errors in Jupyter Notebook; History of Python; Applications of Python; What is Variable-Fundamentals of Python; Rules for Naming Variables in Python; DataTypes in Python; Arithmetic Operation in Python; Various Operations in Python; Comparison Operation in Python; Logical Operations in Python; Identity Operation in Python; Membership Operation in Python; Bitwise Operation in Python; Data Types in Python; Operators in Python; Control Statements in Python; Libraries in Python; Libraries in Python; What is Scipy library; What is Pandas Library; What is Statsmodel and its features;
The course will also cover Data Visualisation & Scikit Learn what Data Visualization is; Matplotib Library; Seaborn Library; Scikit-learn Library What is Dataset and the components of Dataset; Data Collection & Preparation; What's Meant through Data Collection? Understanding Data and Analyzing Data Analysis; Methods of Exploratory Data Analysis; Data Pre-Processing Categorical Variables Data Pre-processing Methods.
The course will also cover what Linear Regression is and the Use of Dataset for Linear Regression; Importing library and load Data Set-up steps in linear regression removing the Index Column-Steps from Linear Regression; Understanding Relationship between Response and Predictors; The Pairplot Method Corr and Heatmap methods explanation; Building a Simple Linear Regression Model; Interpreting the Model Coefficients and Making predictions using our Model Measurement Metric for Model Evaluation; Implementation of Linear Regression: Overview of the Regression; Uploading the Dataset into Jupyter Notebook Importing Libraries and loading Dataset into a Dataframe Eliminate from the Index Column; Exploratory Analysis Relating predictors and responses The creation of a Linear Regression Model; Model Coefficients; Making Predictions. Assessment of Performance.
In the next section, you will learn the Model Evaluation Metrics and Logistic Regression to develop a Diabetes Model.
Content of the Course:
- Explain what Machine Learning does and its importance
- Learn about the various kinds of Descriptive Statistics
- Use and apply Various Operations in Python
- Investigate the use of Two Categories of Supervised Learning
- Learn to distinguish three categories of machine learning. Three Categories of Machine Learning
- Learn about the role of Machine Learning
- Discuss the significance of Probability and its significance
- Define the process by which the Probability Process takes place
- Define the objectives of the Data Gathering Step
- Learn about the various concepts of the concepts of Preparation as well as Data Exploratory Analysis Step
- Define what supervised learning is.
- Differentiate Key Differences Between Supervised, Unsupervised,and Reinforced Learning
- Define the significance of Linear Regression
- Find out the various types of Logistic Regression.
- Find out what is the definition of an Integrated Development Environment and its significance
- Learn the reasons why developers utilize the Integrated Development Environment
- Find out the most crucial aspects in the How-to Addition operations and close the Jupyter Notebook
- Talk about Arithmetic Operation in Python
- Find the various Types of Types Built-in to Data in Python
- Learn the most essential considerations of Dictionaries-Built-in Data types
- Discuss the use of Operations in Python and what it does.
- Recognize the importance of Logical Operators
- Define the various kinds of Controlled Statements.
- Develop and program to calculate the maximum number
- Define the various kinds of function ranges in Python
- Define what is Statistics and Probability along with the most important concepts