Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS
Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS, available at $79.99, has an average rating of 4.5, with 35 lectures, based on 381 reviews, and has 23992 subscribers.
You will learn about Master Machine Learning on Python Make powerful analysis Make accurate predictions Make robust Machine Learning models Use Machine Learning for personal purpose Build an army of powerful Machine Learning models and know how to combine them to solve any problem Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA Clean your input data to remove outliers This course is ideal for individuals who are Beginner Python Developers enthusiastic about Learning Machine Learning and Data Science or Anyone interested in Machine Learning. or Students who have at least high school knowledge in math and who want to start learning Machine Learning. or Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning. or Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets. or Any students in college who want to start a career in Data Science. or Any data analysts who want to level up in Machine Learning. or Any people who want to create added value to their business by using powerful Machine Learning tools. It is particularly useful for Beginner Python Developers enthusiastic about Learning Machine Learning and Data Science or Anyone interested in Machine Learning. or Students who have at least high school knowledge in math and who want to start learning Machine Learning. or Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning. or Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets. or Any students in college who want to start a career in Data Science. or Any data analysts who want to level up in Machine Learning. or Any people who want to create added value to their business by using powerful Machine Learning tools.
Enroll now: Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS
Summary
Title: Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS
Price: $79.99
Average Rating: 4.5
Number of Lectures: 35
Number of Published Lectures: 35
Number of Curriculum Items: 35
Number of Published Curriculum Objects: 35
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- Master Machine Learning on Python
- Make powerful analysis
- Make accurate predictions
- Make robust Machine Learning models
- Use Machine Learning for personal purpose
- Build an army of powerful Machine Learning models and know how to combine them to solve any problem
- Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
- Clean your input data to remove outliers
Who Should Attend
- Beginner Python Developers enthusiastic about Learning Machine Learning and Data Science
- Anyone interested in Machine Learning.
- Students who have at least high school knowledge in math and who want to start learning Machine Learning.
- Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
- Any students in college who want to start a career in Data Science.
- Any data analysts who want to level up in Machine Learning.
- Any people who want to create added value to their business by using powerful Machine Learning tools.
Target Audiences
- Beginner Python Developers enthusiastic about Learning Machine Learning and Data Science
- Anyone interested in Machine Learning.
- Students who have at least high school knowledge in math and who want to start learning Machine Learning.
- Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
- Any students in college who want to start a career in Data Science.
- Any data analysts who want to level up in Machine Learning.
- Any people who want to create added value to their business by using powerful Machine Learning tools.
Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That’s just the average! And it’s not just about money – it’s interesting work too!
Machine Learning (Complete course Overview)
Foundations
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Introduction to Machine Learning
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Intro
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Application of machine learning in different fields.
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Advantage of using Python libraries. (Python for machine learning).
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Python for AI & ML
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Python Basics
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Python functions, packages, and routines.
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Working with Data structure, arrays, vectors & data frames. (Intro Based with some examples)
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Jupyter notebook- installation & function
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Pandas, NumPy, Matplotib, Seaborn
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Applied Stastistics
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Descriptive statistics
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Probability & Conditional Probability
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Hypothesis Testing
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Inferential Statistics
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Probability distributions – Types of distribution – Binomial, Poisson & Normal distribution
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Machine Learning
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Supervised Learning
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Multiple variable Linear regression
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Regression
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Introduction to Regression
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Simple linear regression
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Model Evaluation in Regression Models
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Evaluation Metrics in Regression Models
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Multiple Linear Regression
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Non-Linear Regression
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Naïve bayes classifiers
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Multiple regression
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K-NN classification
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Support vector machines
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Unsupervised Learning
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Intro to Clustering
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K-means clustering
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High-dimensional clustering
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Hierarchical clustering
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Dimension Reduction-PCA
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Classification
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Introduction to Classification
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K-Nearest Neighbours
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Evaluation Metrics in Classification
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Introduction to decision tress
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Building Decision Tress
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Into Logistic regression
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Logistic regression vs Linear Regression
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Logistic Regression training
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Support vector machine
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Ensemble Techniques
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Decision Trees
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Bagging
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Random Forests
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Boosting
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Featurization, Model selection & Tuning
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Feature engineering
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Model performance
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ML pipeline
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Grid search CV
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K fold cross-validation
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Model selection and tuning
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Regularising Linear models
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Bootstrap sampling
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Randomized search CV
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Recommendation Systems
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Introduction to recommendation systems
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Popularity based model
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Hybrid models
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Content based recommendation system
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Collaborative filtering
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Additional Modules
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EDA
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Pandas-profiling library
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Time series forecasting
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ARIMA Approach
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Model Deployment
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Kubernetes
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Capstone Project
If you’ve got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry – and prepare you for a move into this hot career path.
Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won’t find academic, deeply mathematical coverage of these algorithms in this course – the focus is on practical understanding and application of them. At the end, you’ll be given a final project to apply what you’ve learned!
Our Learner’s Review: Excellent course. Precise and well-organized presentation. The complete course is filled with a lot of learning not only theoretical but also practical examples. Mr. Risabh is kind enough to share his practical experiences and actual problems faced by data scientists/ML engineers. The topic of “The ethics of deep learning” is really a gold nugget that everyone must follow. Thank you, 1stMentor and SelfCode Academy for this wonderful course.
Course Curriculum
Chapter 1: Foundation
Lecture 1: Introduction
Lecture 2: Udemy Reviews Update
Chapter 2: Introduction to Machine Learning
Lecture 1: Introduction to Machine Learning
Chapter 3: Applied Statistics
Lecture 1: Statistics 101
Lecture 2: Descriptive Statistics
Lecture 3: Descriptive Statistics (Part-2)
Lecture 4: Measures of Spread
Lecture 5: Probability
Lecture 6: Conditional Probability
Lecture 7: Probability Distribution
Lecture 8: Hypothesis Testing
Chapter 4: ntroduction to Python
Lecture 1: Python Installation
Lecture 2: Python IDE
Lecture 3: Python_Basics
Lecture 4: Python Basics II
Lecture 5: Data Structures
Lecture 6: Numpy
Lecture 7: Pandas
Lecture 8: Data Visualisation
Lecture 9: Data Transformation
Chapter 5: Let's dig Machine Learning
Lecture 1: Machine Learning Intro
Chapter 6: Regression
Lecture 1: Linear Regression
Chapter 7: Classification
Lecture 1: Logistic Regression
Lecture 2: KNN
Lecture 3: Naïve Bayes
Lecture 4: SVM
Lecture 5: Decision Tree
Chapter 8: Clustering
Lecture 1: K-means
Lecture 2: Hierarchical Clustering
Lecture 3: DBScan
Chapter 9: Ensemble ML
Lecture 1: Bagging
Lecture 2: Boosting
Chapter 10: Our Project (Recomendation System)
Lecture 1: PCA
Lecture 2: Recommendations System
Chapter 11: Resourses
Lecture 1: Resourses
Instructors
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Selfcode Academy
Powered by 1stMentor
Rating Distribution
- 1 stars: 5 votes
- 2 stars: 7 votes
- 3 stars: 46 votes
- 4 stars: 96 votes
- 5 stars: 227 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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