|
via Udemy |
Go to Course: https://www.udemy.com/course/machine-learning-in-python/
Certainly! Here's a comprehensive review and recommendation for the Coursera course “Python Machine Learning” bundled with “Python Bootcamp 30 Hours Of Step By Step”: --- ### Course Overview and Content The **Python Machine Learning** and **Python Bootcamp** course offer an extensive learning experience designed to help learners master key concepts in machine learning and Python programming. With a total of over 44 hours of engaging content, practical lessons, quizzes, and downloadable resources, this course provides a comprehensive pathway from basic Python skills to advanced machine learning techniques. **What You Get**: - A detailed 234-page workbook covering reference material - 44 hours of step-by-step instructions - 25 downloadable Python coding files for hands-on learning - 35 quizzes and knowledge checks to reinforce understanding - Active community engagement and progress celebrations - Support and feedback from expert instructors ### Course Content and Structure The course begins with foundational topics such as the history of machine learning, the difference between traditional programming and machine learning, and essential statistical concepts. It then progresses through crucial areas like data preparation, exploration, visualization, and the use of popular libraries like Pandas, SciPy, and Scikit-learn. Learners will explore: - Types of machine learning: supervised, unsupervised, and reinforced learning - Techniques like linear and logistic regression, decision trees, and clustering - Practical implementation in Python, focusing on Jupyter notebooks and IDEs like Anaconda - Building and evaluating models with real datasets, especially in linear regression and classification scenarios ### Instructor and Support Led by Samidha Kurle from Digital Regenesys, the course benefits from her expertise in machine learning and her commitment to student success. The course also involves collaboration with content creator Peter Alkema, creating a well-rounded educational experience. ### Why Recommend This Course? - **Comprehensive Curriculum**: The course covers both theoretical knowledge and practical skills, making it suitable for beginners and those looking to reinforce their understanding. - **Hands-On Approach**: Downloadable Python files and real-world datasets help learners practice coding and build confidence. - **Progress Tracking**: Regular milestones and celebrations encourage motivation and retention. - **Community and Support**: Opportunities to engage with instructors and peers enhance the learning experience. - **Flexibility and Guarantee**: Self-paced access with a 30-day money-back guarantee provides risk-free learning. ### Who Should Enroll? This course is ideal for: - Beginners with no prior technical background - Data enthusiasts looking to expand into machine learning - Professionals seeking practical Python skills for data analysis and predictive modeling - Anyone interested in understanding AI's role in modern business solutions --- ### Final Verdict If you're eager to dive into the world of machine learning and Python programming, this 2-in-1 course on Coursera offers a balanced mix of foundational theory, practical exercises, and real-world applications. The structured content, downloadable resources, and active support make it an excellent investment for anyone aspiring to develop machine learning solutions for real-life problems. **Highly Recommended!** Enroll today to start your journey in mastering Python for machine learning and data analysis, and take advantage of the flexible, comprehensive learning experience it provides. --- Would you like a shorter summary or specific tips on enrolling?
Course 1: Python Machine Learning > Section 1 - Section 68Course 2: Python Bootcamp 30 Hours Of Step By Step > Section 69 - 94Everything you get with this 2 in 1 course:234-page Machine Learning workbook containing all the reference material44 hours of clear and concise step by step instructions, practical lessons and engagement25 Python coding files so you can download and follow along in the bootcamp to enhance your learning35 quizzes and knowledge checks at various stages to test your learning and confirm your growthIntroduce yourself to our community of students in this course and tell us your goalsEncouragement & celebration of your progress: 25%, 50%, 75% and then 100% when you get your certificateThis 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 importanceUnderstand the Role of Machine LearningExplain what is StatisticsLearn the different types of Descriptive StatisticsExplain the meaning of Probability and its importanceDefine how Probability Process happensDiscuss the definition of Objectives and Data Gathering StepKnow the different concepts of Data Preparation and Data Exploratory Analysis StepDefine what is Supervised LearningDifferentiate Key Differences Between Supervised, Unsupervised, and Reinforced LearningLearn the difference between the Three Categories of Machine LearningExplore the usage of Two Categories of Supervised LearningExplain the importance of Linear RegressionLearn the different types of Logistic RegressionLearn what is an Integrated Development Environment and its importanceUnderstand the factors why Developers use Integrated Development EnvironmentLearn the most important factors on How to Perform Addition operations and close the Jupyter NotebookApply and use Various Operations in PythonDiscuss Arithmetic Operation in PythonIdentify the different types of Built-in-Data Types in PythonLearn the most important considerations of Dictionaries-Built-in Data typesExplain the usage of Operations in Python and its importanceUnderstand the importance of Logical OperatorsDefine the different types of Controlled StatementsBe able to create and write a program to find the maximum number...and more!Contents and OverviewYou'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.You'll get premium support and feedback to help you become more confident with finance!Our happiness guarantee...We have a 30-day 100% money-back guarantee, so if you aren't happy with your purchase, we will refund your course - no questions asked!We can't wait to see you on the course!Enrol now, and master Machine Learning!Peter and Samidha