Machine Learning in Python

Get instant access to a 234-page Machine Learning workbook containing all the reference material

Over 12 hours of clear and concise step by step instructions, practical lessons and engagement

35 quizzes and knowledge checks at various stages to test your learning and confirm your growth

Introduce yourself to our community of students in this course and tell us your goals

Encouragement & celebration of your progress: 25%, 50%, 75% and then 100% when you get your certificate

This course will help you develop Machine Learning skills for solving real-life problems in the new digital world. Machine Learning combines computer science and statistics to analyze raw real-time data, identify trends, and make predictions. The participants will explore key techniques and tools to build Machine Learning solutions for businesses. You don’t need to have any technical knowledge to learn this skill.

What will you learn:

• Define what Machine Learning does and its importance

• Understand the Role of Machine Learning

• Explain what is Statistics

• Learn the different types of Descriptive Statistics

• Explain the meaning of Probability and its importance

• Define how Probability Process happens

• Discuss the definition of Objectives and Data Gathering Step

• Know the different concepts of Data Preparation and Data Exploratory Analysis Step

• Define what is Supervised Learning

• Differentiate Key Differences Between Supervised, Unsupervised, and Reinforced Learning

• Learn the difference between the Three Categories of Machine Learning

• Explore the usage of Two Categories of Supervised Learning

• Explain the importance of Linear Regression

• Learn the different types of Logistic Regression

• Learn what is an Integrated Development Environment and its importance

• Understand the factors why Developers use Integrated Development Environment

• Learn the most important factors on How to Perform Addition operations and close the Jupyter Notebook

• Apply and use Various Operations in Python

• Discuss Arithmetic Operation in Python

• Identify the different types of Built-in-Data Types in Python

• Learn the most important considerations of Dictionaries-Built-in Data types

• Explain the usage of Operations in Python and its importance

• Understand the importance of Logical Operators

• Define the different types of Controlled Statements

• Be able to create and write a program to find the maximum number

• ...and more!

Contents and Overview

You'll start with the History of Machine Learning; Difference Between Traditional Programming and Machine Learning; What does Machine Learning do; Definition of Machine Learning; Apply Apple Sorting Example Experiences; Role of Machine Learning; Machine Learning Key Terms; Basic Terminologies of Statistics; Descriptive Statistics-Types of Statistics; Types of Descriptive Statistics; What is Inferential Statistics; What is Analysis and its types; Probability and Real-life Examples; How Probability is a Process; Views of Probability; Base 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;

This course will also tackle Data Visualisation & Scikit Learn; What is Data Visualization; Matplotib Library; Seaborn Library; Scikit-learn Library; What is Dataset; Components of Dataset; Data Collection & Preparation; What is Meant by Data Collection; Understanding Data; Exploratory Data Analysis; Methods of Exploratory Data Analysis; Data Pre-Processing; Categorical Variables; Data Pre-processing Techniques.

This course will also discuss What is Linear Regression and its Use Case; Dataset For Linear Regression; Import library and Load Data set- steps of linear regression; Remove the Index Column-Steps of Linear Regression; Exploring Relationship between Predictors and Response; Pairplot method explanation; Corr and Heatmap method explanation; Creating Simple Linear Regression Model; Interpreting Model Coefficients; Making Predictions with our Model; Model Evaluation Metric; Implementation of Linear Regression-lecture overview; Uploading the Dataset in Jupyter Notebook; Importing Libraries and Load Dataset into Dataframe; Remove the Index Column; Exploratory Analysis -relation of predictor and response; Creation of Linear Regression Model; Model Coefficients; Making Predictions; Evaluation of Model Performance.

Next, you will learn about Model Evaluation Metrics and Logistic Regression - Diabetes Model.

Who are the Instructors?

Samidha Kurle from Digital Regenesys is your lead instructor – a professional making a living from her teaching skills with expertise in Machine Learning. She has joined with content creator Peter Alkema to bring you this amazing new course.

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Enrol now, and master Machine Learning!

Peter and Samidha