English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 8.5 Hours | 4.04 GB
Machine Learning – Get Your Hands Dirty by Solving Real Industry Challenges with Python
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 not one 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!
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.
What you’ll 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
Table of Contents
Introduction
1 Welcome to the course!
2 Where to get the materials
Breast Cancer Classification
3 Introduction
4 Business Challenge
5 Updates on Udemy Reviews
6 Challenge in Machine Learning Vocabulary
7 Data Visualisation
8 Model Training
9 Model Evaluation
10 Improving the Model
11 Conclusion
Fashion Class Classification
12 Business Challenge
13 Challenge in Machine Learning Vocabulary
14 Data Visualisation
15 Model Training Part I
16 Model Training Part II
17 Model Training Part III
18 Model Training Part IV
19 Model Evaluation
20 Improving the Model
21 Conclusion
Directing Customers to Subscription Through App Behavior Analysis
22 Fintech Case Studies Introduction
23 Introduction
24 Data
25 Features Histograms
26 Correlation Plot
27 Correlation Matrix
28 Feature Engineering – Response
29 Feature Engineering – Screens
30 Data Pre-Processing
31 Model Building
32 Model Conclusion
33 Final Remarks
Minimizing Churn Rate Through Analysis of Financial Habits
34 Introduction
35 Data
36 Data Cleaning
37 Features Histograms
38 Pie Chart Distributions
39 Correlation Plot
40 Correlation Matrix
41 One-Hot Encoding
42 Feature Scaling & Balancing
43 Model Building
44 K-Fold Cross Validation
45 Feature Selection
46 Model Conclusion
47 Final Remarks
Predicting the Likelihood of E-Signing a Loan Based on Financial History
48 Introduction
49 Data
50 Data Housekeeping
51 Histograms
52 Correlation Plot
53 Correlation Matrix
54 Feature Engineering
55 Data Preprocessing
56 Model Building Part 1
57 Model Building Part 2
58 Grid Search Part 1
59 Grid Search Part 2
60 Model Conclusion
61 Final Remarks
Credit Card Fraud Detection
62 Case Study
63 Machine Learning Vocabulary
64 Set Up
65 Data Visualization
66 Data Preprocessing
67 Deep Learning Part 1
68 Deep Learning Part 2
69 Splitting the Data
70 Training
71 Metrics
72 Confusion Matrix
73 Machine Learning Classifiers
74 Random Forest
75 Decision Trees
76 Sampling
77 Undersampling
78 Smote
79 Final remarks
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