Machine Learning Practical: 6 Real-World Applications
Machine Learning Practical: 6 Real-World Applications, available at $79.99, has an average rating of 4.54, with 94 lectures, 1 quizzes, based on 3039 reviews, and has 23168 subscribers.
You will learn about You will know how real data science project looks like You will be able to include these Case Studies in your resume You will be able better market yourself as a Machine Learning Practioneer You will feel confident during Data Science interview You will learn how to chain multiple ML algorithms together to achieve the goal You will learn most advanced Data Visualization techniques with Seaborn and Matplotlib You will learn Logistic Regression You will learn L1 Regularization (Lasso) You will learn Random Forest Classifier This course is ideal for individuals who are Data Science and Machine Learning enthusiasts who want to understand how real data science projects look like. or Anyone with Machine Learning and Python knowledge who wants to practice their skills It is particularly useful for Data Science and Machine Learning enthusiasts who want to understand how real data science projects look like. or Anyone with Machine Learning and Python knowledge who wants to practice their skills.
Enroll now: Machine Learning Practical: 6 Real-World Applications
Summary
Title: Machine Learning Practical: 6 Real-World Applications
Price: $79.99
Average Rating: 4.54
Number of Lectures: 94
Number of Quizzes: 1
Number of Published Lectures: 81
Number of Curriculum Items: 95
Number of Published Curriculum Objects: 81
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- You will know how real data science project looks like
- You will be able to include these Case Studies in your resume
- You will be able better market yourself as a Machine Learning Practioneer
- You will feel confident during Data Science interview
- You will learn how to chain multiple ML algorithms together to achieve the goal
- You will learn most advanced Data Visualization techniques with Seaborn and Matplotlib
- You will learn Logistic Regression
- You will learn L1 Regularization (Lasso)
- You will learn Random Forest Classifier
Who Should Attend
- Data Science and Machine Learning enthusiasts who want to understand how real data science projects look like.
- Anyone with Machine Learning and Python knowledge who wants to practice their skills
Target Audiences
- Data Science and Machine Learning enthusiasts who want to understand how real data science projects look like.
- Anyone with Machine Learning and Python knowledge who wants to practice their skills
So you know the theory of Machine Learning and know how to create your first algorithms. Now what?
There are tons of courses out there about the underlying theory of Machine Learning which don’t go any deeper – into the applications.
This course is notone of them.
Are you ready to apply all of the theory and knowledge to real life Machine Learning challenges?
Then welcome to “Machine Learning Practical”.
We gathered best industry professionals with tons of completed projects behind.
Each presenter has a unique style, which is determined by his experience, and like in a real world, you will need adjust to it if you want successfully complete this course. We will leave no one behind!
This course will demystify how real Data Science project looks like. Time to move away from these polished examples which are only introducing you to the matter, but not giving any real experience.
If you are still dreaming where to learn Machine Learning through practice, where to take real-life projects for your CV, how to not look like a noob in the recruiter’s eyes, then you came to the right place!
This course provides a hands-on approach to real-life challenges and covers exactly what you need to succeed in the real world of Data Science.
There are most exciting case studies including:
● diagnosing diabetes in the early stages
● directing customers to subscription products with app usage analysis
● minimizing churn rate in finance
● predicting customer location with GPS data
● forecasting future currency exchange rates
● classifying fashion
● predicting breast cancer
● and much more!
All real.
All true.
All helpful and applicable.
And another extra:
In this course we will also cover Deep Learning Techniques and their practical applications.
So as you can see, our goal here is to really build the World’s leading practical machine learning course.
If your goal is to become a Machine Learning expert, you know how valuable these real-life examples really are.
They will determine the difference between Data Scientists who just know the theory and Machine Learning experts who have gotten their hands dirty.
So if you want to get hands-on experience which you can add to your portfolio, then this course is for you.
Enroll now and we’ll see you inside.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Welcome to the course!
Lecture 2: Learning Paths
Lecture 3: Where to get the materials
Chapter 2: Breast Cancer Classification
Lecture 1: Introduction
Lecture 2: Business Challenge
Lecture 3: Challenge in Machine Learning Vocabulary
Lecture 4: Data Visualisation
Lecture 5: Model Training
Lecture 6: Model Evaluation
Lecture 7: Improving the Model
Lecture 8: Conclusion
Chapter 3: Fashion Class Classification
Lecture 1: Business Challenge
Lecture 2: Challenge in Machine Learning Vocabulary
Lecture 3: Data Visualisation
Lecture 4: Model Training Part I
Lecture 5: Model Training Part II
Lecture 6: Model Training Part III
Lecture 7: Model Training Part IV
Lecture 8: Model Evaluation
Lecture 9: Improving the Model
Lecture 10: Conclusion
Chapter 4: Directing Customers to Subscription Through App Behavior Analysis
Lecture 1: Fintech Case Studies Introduction
Lecture 2: Introduction
Lecture 3: Data
Lecture 4: Features Histograms
Lecture 5: Correlation Plot
Lecture 6: Correlation Matrix
Lecture 7: Feature Engineering – Response
Lecture 8: Feature Engineering – Screens
Lecture 9: Data Pre-Processing
Lecture 10: Model Building
Lecture 11: Model Conclusion
Lecture 12: Final Remarks
Chapter 5: Minimizing Churn Rate Through Analysis of Financial Habits
Lecture 1: Introduction
Lecture 2: Data
Lecture 3: Data Cleaning
Lecture 4: Features Histograms
Lecture 5: Pie Chart Distributions
Lecture 6: Correlation Plot
Lecture 7: Correlation Matrix
Lecture 8: One-Hot Encoding
Lecture 9: Feature Scaling & Balancing
Lecture 10: Model Building
Lecture 11: K-Fold Cross Validation
Lecture 12: Feature Selection
Lecture 13: Model Conclusion
Lecture 14: Final Remarks
Chapter 6: Predicting the Likelihood of E-Signing a Loan Based on Financial History
Lecture 1: Introduction
Lecture 2: Data
Lecture 3: Data Housekeeping
Lecture 4: Histograms
Lecture 5: Correlation Plot
Lecture 6: Correlation Matrix
Lecture 7: Feature Engineering
Lecture 8: Data Preprocessing
Lecture 9: Model Building Part 1
Lecture 10: Model Building Part 2
Lecture 11: Grid Search Part 1
Lecture 12: Grid Search Part 2
Lecture 13: Model Conclusion
Lecture 14: Final Remarks
Chapter 7: Credit Card Fraud Detection
Lecture 1: Case Study
Lecture 2: Machine Learning Vocabulary
Lecture 3: Set Up
Lecture 4: Data Visualization
Lecture 5: Data Preprocessing
Lecture 6: Deep Learning Part 1
Lecture 7: Deep Learning Part 2
Lecture 8: Splitting the Data
Lecture 9: Training
Lecture 10: Metrics
Lecture 11: Confusion Matrix
Lecture 12: Machine Learning Classifiers
Lecture 13: Random Forest
Lecture 14: Decision Trees
Lecture 15: Sampling
Lecture 16: Undersampling
Lecture 17: Smote
Lecture 18: Final remarks
Lecture 19: THANK YOU Video
Chapter 8: Congratulations!! Don't forget your Prize 🙂
Lecture 1: Bonus: How To UNLOCK Top Salaries (Live Training)
Instructors
-
SuperDataScience Team
Helping Data Scientists Succeed -
Rony Sulca
Senior Product Analyst at Influur -
Ligency Team
Helping Data Scientists Succeed
Rating Distribution
- 1 stars: 70 votes
- 2 stars: 103 votes
- 3 stars: 366 votes
- 4 stars: 1007 votes
- 5 stars: 1493 votes
Frequently Asked Questions
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