Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]
 | 42h 48m total length |
 | 46 sections |
| 386 lectures |
Course Overview
Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]
Explore the world of Machine Learning with this Machine Learning A-Z course designed by experts. Trusted by over 1 million students globally, it simplifies complex theory, algorithms, and coding libraries. Learn step-by-step with tutorials in Python, R, or both, and build the skills needed for a successful career in Data Science
What you’ll learn:
- Master Machine Learning on Python & R
- Have a great intuition of many Machine Learning models
- Make accurate predictions
- Make powerful analysis
- Make robust Machine Learning models
- Create strong added value to your business
- Use Machine Learning for personal purpose
- Handle specific topics like Reinforcement Learning, NLP and Deep Learning
- Handle advanced techniques like Dimensionality Reduction
- Know which Machine Learning model to choose for each type of problem
- Build an army of powerful Machine Learning models and know how to combine them to solve any problem
Course Title
Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]
This course includes:
- 42.5 hours on-demand video
- 5 coding exercises
- 40 articles
- 9 downloadable resources
- Access on mobile and TV
- Full lifetime access
- Certificate of completion
Requirements
- Just some high school mathematics level.
Who this course is for:
- 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 are not satisfied with their job and who want to become a Data Scientist.
- Any people who want to create added value to their business by using powerful Machine Learning tools.
Course content
46 sections • 386 lectures • 42h 48m total length
1. Welcome to the course! Here we will help you get started in the best conditions – 6 lectures • 19min
| Welcome Challenge! | 2:43 |
| Machine Learning Demo – Get Excited! Preview | 04:45 |
| Get all the Datasets, Codes and Slides here Preview | 00:09 |
| How to use the ML A-Z folder & Google Colab | 5:44 |
| Installing R and R Studio (Mac, Linux & Windows) | 5:21 |
| EXTRA: Use ChatGPT to Boost your ML Skills | 0:24 |
2. ——————– Part 1: Data Preprocessing ——————– – 4 lectures • 10min
| Welcome to Part 1 – Data Preprocessing | 0:22 |
| The Machine Learning process Preview | 01:31 |
| Splitting the data into a Training and Test set | 2:02 |
| Feature Scaling | 6:27 |
3. Data Preprocessing in Python – 19 lectures • 1hr 32min
| Getting Started – Step 1 | 5:21 |
| Getting Started – Step 2 | 5:21 |
| Importing the Libraries | 3:34 |
| Importing the Dataset – Step 1 | 5:13 |
| Importing the Dataset – Step 2 | 4:42 |
| Importing the Dataset – Step 3 | 5:46 |
| For Python learners, summary of Object-oriented programming: classes & objects | 1:03 |
| Coding Exercise 1: Importing and Preprocessing a Dataset for Machine Learning | 1 question |
| Taking care of Missing Data – Step 1 | 5:56 |
| Taking care of Missing Data – Step 2 | 5:58 |
| Coding Exercise 2: Handling Missing Data in a Dataset for Machine Learning | 1 question |
| Encoding Categorical Data – Step 1 | 4:24 |
| Encoding Categorical Data – Step 2 | 5:54 |
| Encoding Categorical Data – Step 3 | 4:39 |
| Coding Exercise 3: Encoding Categorical Data for Machine Learning | 1 question |
| Splitting the dataset into the Training set and Test set – Step 1 | 3:55 |
| Splitting the dataset into the Training set and Test set – Step 2 | 5:59 |
| Splitting the dataset into the Training set and Test set – Step 3 | 3:52 |
| Coding Exercise 4: Dataset Splitting and Feature Scaling | 1 question |
| Feature Scaling – Step 1 | 5:56 |
| Feature Scaling – Step 2 | 4:45 |
| Feature Scaling – Step 3 | 3:48 |
| Feature Scaling – Step 4 | 5:59 |
| Coding exercise 5: Feature scaling for Machine Learning | 1 question |
4. Data Preprocessing in R – 10 lectures • 42min
| Getting Started | 1:35 |
| Dataset Description | 1:57 |
| Importing the Dataset | 2:44 |
| Taking care of Missing Data | 5:55 |
| Encoding Categorical Data | 5:56 |
| Splitting the dataset into the Training set and Test set – Step 1 | 4:38 |
| Splitting the dataset into the Training set and Test set – Step 2 | 4:54 |
| Feature Scaling – Step 1 | 4:25 |
| Feature Scaling – Step 2 | 4:49 |
| Data Preprocessing Template | 5:15 |
| Data Preprocessing Quiz | 5 questions |
5. ——————– Part 2: Regression ——————– – 1 lecture • 1min
| Welcome to Part 2 – Regression | Welcome to Part 2 – Regression | 0:21 |
6. Simple Linear Regression – 16 lectures • 1hr 12min
| Simple Linear Regression Intuition | 2:22 |
| Ordinary Least Squares Preview | 03:17 |
| Simple Linear Regression in Python – Step 1a | 5:49 |
| Simple Linear Regression in Python – Step 1b | 5:58 |
| Simple Linear Regression in Python – Step 2a | 3:53 |
| Simple Linear Regression in Python – Step 2b | 3:58 |
| Simple Linear Regression in Python – Step 3 | 4:35 |
| Simple Linear Regression in Python – Step 4a | 5:49 |
| Simple Linear Regression in Python – Step 4b | 5:57 |
| Simple Linear Regression in Python – Additional Lecture | 0:30 |
| Simple Linear Regression in R – Step 1 | 4:40 |
| Simple Linear Regression in R – Step 2 | 5:58 |
| Simple Linear Regression in R – Step 3 | 3:38 |
| Simple Linear Regression in R – Step 4a | 5:44 |
| Simple Linear Regression in R – Step 4b | 5:33 |
| Simple Linear Regression in R – Step 4c | 4:37 |
| Simple Linear Regression Quiz | 5 questions |
7. Multiple Linear Regression – 25 lectures • 2hr 16min
| Dataset + Business Problem Description | 3:44 |
| Multiple Linear Regression Intuition | 2:26 |
| Assumptions of Linear Regression Preview | 04:23 |
| Multiple Linear Regression Intuition – Step 3 | 7:21 |
| Multiple Linear Regression Intuition – Step 4 | 2:10 |
| Understanding the P-Value | 11:44 |
| Multiple Linear Regression Intuition – Step 5 | 15:41 |
| Multiple Linear Regression in Python – Step 1a | 5:54 |
| Multiple Linear Regression in Python – Step 1b | 2:35 |
| Multiple Linear Regression in Python – Step 2a | 4:28 |
| Multiple Linear Regression in Python – Step 2b | 4:43 |
| Multiple Linear Regression in Python – Step 3a | 5:52 |
| Multiple Linear Regression in Python – Step 3b | 4:32 |
| Multiple Linear Regression in Python – Step 4a | 5:38 |
| Multiple Linear Regression in Python – Step 4b | 5:34 |
| Multiple Linear Regression in Python – Backward Elimination | 1:35 |
| Multiple Linear Regression in Python – EXTRA CONTENT | 0:31 |
| Multiple Linear Regression in R – Step 1a | 3:53 |
| Multiple Linear Regression in R – Step 1b | 3:57 |
| Multiple Linear Regression in R – Step 2a | 5:22 |
| Multiple Linear Regression in R – Step 2b | 4:20 |
| Multiple Linear Regression in R – Step 3 | 4:26 |
| Multiple Linear Regression in R – Backward Elimination – HOMEWORK ! | 17:51 |
| Multiple Linear Regression in R – Backward Elimination – Homework Solution | 7:33 |
| Multiple Linear Regression in R – Automatic Backward Elimination | 0:15 |
| Multiple Linear Regression Quiz | 5 questions |
8. Polynomial Regression – 20 lectures • 1hr 39min
| Polynomial Regression Intuition | 5:08 |
| Polynomial Regression in Python – Step 1a | 4:36 |
| Polynomial Regression in Python – Step 1b | 5:55 |
| Polynomial Regression in Python – Step 2a | 5:55 |
| Polynomial Regression in Python – Step 2b | 5:43 |
| Polynomial Regression in Python – Step 3a | 5:57 |
| Polynomial Regression in Python – Step 3b | 5:38 |
| Polynomial Regression in Python – Step 4a | 3:59 |
| Polynomial Regression in Python – Step 4b | 3:59 |
| Polynomial Regression in R – Step 1a | 3:45 |
| Polynomial Regression in R – Step 1b | 3:39 |
| Polynomial Regression in R – Step 2a | 4:40 |
| Polynomial Regression in R – Step 2b | 4:55 |
| Polynomial Regression in R – Step 3a | 4:59 |
| Polynomial Regression in R – Step 3b | 5:31 |
| Polynomial Regression in R – Step 3c | 5:42 |
| Polynomial Regression in R – Step 4a | 3:58 |
| Polynomial Regression in R – Step 4b | 3:47 |
| R Regression Template – Step 1 | 5:57 |
| R Regression Template – Step 2 | 5:25 |
| Polynomial Regression Quiz | 5 questions |
9. Support Vector Regression (SVR) – 13 lectures • 1hr 3min
| SVR Intuition (Updated!) Preview | 08:09 |
| Heads-up on non-linear SVR Preview | 03:57 |
| SVR in Python – Step 1a | 5:46 |
| SVR in Python – Step 1b | 3:29 |
| SVR in Python – Step 2a | 5:34 |
| SVR in Python – Step 2b | 4:56 |
| SVR in Python – Step 2c | 3:31 |
| SVR in Python – Step 3 | 5:57 |
| SVR in Python – Step 4 | 3:46 |
| SVR in Python – Step 5a | 3:42 |
| SVR in Python – Step 5b | 3:40 |
| SVR in R – Step 1 | 5:58 |
| SVR in R – Step 2 | 4:58 |
| SVR Quiz | 5 questions |
10. Decision Tree Regression – 10 lectures • 52min
| Decision Tree Regression Intuition | 11:06 |
| Decision Tree Regression in Python – Step 1a | 4:40 |
| Decision Tree Regression in Python – Step 1b | 3:58 |
| Decision Tree Regression in Python – Step 2 | 4:59 |
| Decision Tree Regression in Python – Step 3 | 3:16 |
| Decision Tree Regression in Python – Step 4 | 4:59 |
| Decision Tree Regression in R – Step 1 | 4:55 |
| Decision Tree Regression in R – Step 2 | 5:49 |
| Decision Tree Regression in R – Step 3 | 4:55 |
| Decision Tree Regression in R – Step 4 | 3:50 |
| Decision Tree Regression Quiz | 5 questions |
11. Random Forest Regression – 6 lectures • 36min
| Random Forest Regression Intuition | 6:44 |
| Random Forest Regression in Python – Step 1 | 5:53 |
| Random Forest Regression in Python – Step 2 | 5:55 |
| Random Forest Regression in R – Step 1 | 5:51 |
| Random Forest Regression in R – Step 2 | 5:58 |
| Random Forest Regression in R – Step 3 | 5:26 |
| Random Forest Regression Quiz | 5 questions |
12. Evaluating Regression Models Performance – 2 lectures • 10min
| R-Squared Intuition Preview | 04:35 |
| Adjusted R-Squared Intuition | 5:30 |
| Evaluating Regression Models Performance Quiz | 5 questions |
13. Regression Model Selection in Python – 8 lectures • 29min
| Make sure you have this Model Selection folder ready | 0:31 |
| Preparation of the Regression Code Templates – Step 1 | 4:45 |
| Preparation of the Regression Code Templates – Step 2 | 5:59 |
| Preparation of the Regression Code Templates – Step 3 | 3:59 |
| Preparation of the Regression Code Templates – Step 4 | 3:58 |
| THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! – STEP 1 | 4:47 |
| THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! – STEP 2 | 4:15 |
| Conclusion of Part 2 – Regression | 1:03 |
14. Regression Model Selection in R – 3 lectures • 19min
| Evaluating Regression Models Performance – Homework’s Final Part | 8:54 |
| Interpreting Linear Regression Coefficients | 9:16 |
| Conclusion of Part 2 – Regression | 1:03 |
15. ——————– Part 3: Classification ——————– – 2 lectures • 3min
| Welcome to Part 3 – Classification | 0:21 |
| What is Classification? | 2:30 |
16. Logistic Regression – 29 lectures • 1hr 55min
| Logistic Regression Intuition | 04:55 |
| Maximum Likelihood | 3:50 |
| Logistic Regression in Python – Step 1a | 5:43 |
| Logistic Regression in Python – Step 1b | 3:59 |
| Logistic Regression in Python – Step 2a | 5:51 |
| Logistic Regression in Python – Step 2b | 5:57 |
| Logistic Regression in Python – Step 3a | 3:58 |
| Logistic Regression in Python – Step 3b | 3:30 |
| Logistic Regression in Python – Step 4a | 5:59 |
| Logistic Regression in Python – Step 4b | 1:49 |
| Logistic Regression in Python – Step 5 | 5:59 |
| Logistic Regression in Python – Step 6a | 5:52 |
| Logistic Regression in Python – Step 6b | 3:33 |
| Logistic Regression in Python – Step 7a | 5:54 |
| Logistic Regression in Python – Step 7b | 3:44 |
| Logistic Regression in Python – Step 7c | 3:19 |
| Logistic Regression in Python – Step 7 (Colour-blind friendly image) | 0:12 |
| Logistic Regression in R – Step 1 | 5:58 |
| Logistic Regression in R – Step 2 | 2:58 |
| Logistic Regression in R – Step 3 | 5:23 |
| Logistic Regression in R – Step 4 Preview | 2:48 |
| Warning – Update | 0:38 |
| Logistic Regression in R – Step 5a | 5:48 |
| Logistic Regression in R – Step 5b | 5:59 |
| Logistic Regression in R – Step 5c | 4:59 |
| Logistic Regression in R – Step 5 (Colour-blind friendly image) | 0:12 |
| R Classification Template | 5:22 |
| Machine Learning Regression and Classification EXTRA | 0:17 |
| Logistic Regression Quiz | 5 questions |
| EXTRA CONTENT: Logistic Regression Practical Case Study | 0:16 |
17. K-Nearest Neighbors (K-NN) – 7 lectures • 38min
| K-Nearest Neighbor Intuition | 4:52 |
| K-NN in Python – Step 1 | 5:58 |
| K-NN in Python – Step 2 | 5:51 |
| K-NN in Python – Step 3 | 5:58 |
| K-NN in R – Step 1 | 5:54 |
| K-NN in R – Step 2 | 4:33 |
| K-NN in R – Step 3 | 4:44 |
| K-Nearest Neighbor Quiz | 5 questions |
18. Support Vector Machine (SVM) – 6 lectures • 36min
| SVM Intuition | 9:49 |
| SVM in Python – Step 1 | 5:58 |
| SVM in Python – Step 2 | 5:53 |
| SVM in Python – Step 3 | 2:39 |
| SVM in R – Step 1 | 5:47 |
| SVM in R – Step 2 | 5:27 |
| SVM Quiz | 5 questions |
19. Kernel SVM – 10 lectures • 1hr 5min
| Kernel SVM Intuition | 3:17 |
| Mapping to a higher dimension Preview | 07:50 |
| The Kernel Trick | 12:20 |
| Types of Kernel Functions | 2:24 |
| Non-Linear Kernel SVR (Advanced) | 10:55 |
| Kernel SVM in Python – Step 1 | 5:59 |
| Kernel SVM in Python – Step 2 | 5:59 |
| Kernel SVM in R – Step 1 | 5:42 |
| Kernel SVM in R – Step 2 | 5:41 |
| Kernel SVM in R – Step 3 | 4:58 |
| Kernel SVM Quiz | 5 questions |
20. Naive Bayes – 10 lectures • 1hr 17min
| Bayes Theorem Preview | 20:25 |
| Naive Bayes Intuition Preview | 14:03 |
| Naive Bayes Intuition (Challenge Reveal) | 6:04 |
| Naive Bayes Intuition (Extras) | 9:41 |
| Naive Bayes in Python – Step 1 | 5:56 |
| Naive Bayes in Python – Step 2 | 5:48 |
| Naive Bayes in Python – Step 3 | 1:35 |
| Naive Bayes in R – Step 1 | 4:53 |
| Naive Bayes in R – Step 2 | 4:41 |
| Naive Bayes in R – Step 3 | 3:29 |
| Naive Bayes Quiz | 5 questions |
21. Decision Tree Classification – 6 lectures • 38min
| Decision Tree Classification Intuition | 8:08 |
| Decision Tree Classification in Python – Step 1 | 5:59 |
| Decision Tree Classification in Python – Step 2 | 5:56 |
| Decision Tree Classification in R – Step 1 | 5:55 |
| Decision Tree Classification in R – Step 2 | 5:51 |
| Decision Tree Classification in R – Step 3 | 5:42 |
| Decision Tree Classification Quiz | 5 questions |
22. Random Forest Classification – 6 lectures • 34min
| Random Forest Classification Intuition | 4:28 |
| Random Forest Classification in Python – Step 1 | 5:56 |
| Random Forest Classification in Python – Step 2 | 5:56 |
| Random Forest Classification in R – Step 1 | 5:56 |
| Random Forest Classification in R – Step 2 | 5:58 |
| Random Forest Classification in R – Step 3 | 5:26 |
| Random Forest Classification Quiz | 5 questions |
23. Classification Model Selection in Python – 6 lectures • 26min
| Make sure you have this Model Selection folder ready | 0:33 |
| Confusion Matrix & Accuracy Ratios Preview | 04:52 |
| ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION – STEP 1 | 5:51 |
| ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION – STEP 2 | 5:59 |
| ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION – STEP 3 | 5:52 |
| ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION – STEP 4 | 2:38 |
24. Evaluating Classification Models Performance – 5 lectures • 30min
| False Positives & False Negatives | 7:57 |
| Accuracy Paradox | 2:12 |
| CAP Curve | 11:16 |
| CAP Curve Analysis | 6:19 |
| Conclusion of Part 3 – Classification | 2:09 |
| Evaluating Classiification Model Performance Quiz | 5 questions |
25. ——————– Part 4: Clustering ——————–1 lecture • 1min
| Welcome to Part 4 – Clustering | 0:21 |
26. K-Means Clustering – 17 lectures • 1hr 24min
| What is Clustering? (Supervised vs Unsupervised Learning) Preview | 03:19 |
| K-Means Clustering Intuition | 2:37 |
| The Elbow Method | 3:59 |
| K-Means++ | 4:48 |
| K-Means Clustering in Python – Step 1a | 4:59 |
| K-Means Clustering in Python – Step 1b | 2:58 |
| K-Means Clustering in Python – Step 2a | 4:55 |
| K-Means Clustering in Python – Step 2b | 5:25 |
| K-Means Clustering in Python – Step 3a | 5:59 |
| K-Means Clustering in Python – Step 3b | 5:57 |
| K-Means Clustering in Python – Step 3c | 3:58 |
| K-Means Clustering in Python – Step 4 | 5:58 |
| K-Means Clustering in Python – Step 5a | 5:59 |
| K-Means Clustering in Python – Step 5b | 4:57 |
| K-Means Clustering in Python – Step 5c | 6:59 |
| K-Means Clustering in R – Step 1 | 5:59 |
| K-Means Clustering in R – Step 2 | 5:39 |
| K-Means Clustering Quiz | 5 questions |
27. Hierarchical Clustering – 15 lectures • 1hr 21min
| Hierarchical Clustering Intuition Preview | 08:47 |
| Hierarchical Clustering How Dendrograms Work | 8:47 |
| Hierarchical Clustering Using Dendrograms | 11:21 |
| Hierarchical Clustering in Python – Step 1 | 5:58 |
| Hierarchical Clustering in Python – Step 2a | 4:52 |
| Hierarchical Clustering in Python – Step 2b | 5:58 |
| Hierarchical Clustering in Python – Step 2c | 5:59 |
| Hierarchical Clustering in Python – Step 3a | 5:45 |
| Hierarchical Clustering in Python – Step 3b | 5:42 |
| Hierarchical Clustering in R – Step 1 | 3:45 |
| Hierarchical Clustering in R – Step 2 | 5:23 |
| Hierarchical Clustering in R – Step 3 Preview | 03:18 |
| Hierarchical Clustering in R – Step 4 | 2:45 |
| Hierarchical Clustering in R – Step 5 | 2:33 |
| Hierarchical Clustering Quiz | 5 questions |
| Conclusion of Part 4 – Clustering | 0:12 |
28. ——————– Part 5: Association Rule Learning ——————–1 lecture • 1min
| Welcome to Part 5 – Association Rule Learning | 0:11 |
29. Apriori – 8 lectures • 2hr 10min
| Apriori Intuition | 18:13 |
| Apriori in Python – Step 1 | 8:46 |
| Apriori in Python – Step 2 | 17:07 |
| Apriori in Python – Step 3 | 12:48 |
| Apriori in Python – Step 4 | 19:41 |
| Apriori in R – Step 1 | 19:53 |
| Apriori in R – Step 2 | 14:24 |
| Apriori in R – Step 3 | 19:17 |
| Apriori Quiz | 5 questions |
30. Eclat – 3 lectures • 28min
| Eclat Intuition | 6:05 |
| Eclat in Python | 12:00 |
| Eclat in R | 10:09 |
| Eclat Quiz | 5 questions |
31. ——————– Part 6: Reinforcement Learning ——————–1 lecture • 1min
| Welcome to Part 6 – Reinforcement Learning | 0:41 |
32. Upper Confidence Bound (UCB) – 13 lectures • 2hr 22min
| The Multi-Armed Bandit Problem Preview | 15:36 |
| Upper Confidence Bound (UCB) Intuition Preview | 14:53 |
| Upper Confidence Bound in Python – Step 1 | 12:42 |
| Upper Confidence Bound in Python – Step 2 | 3:51 |
| Upper Confidence Bound in Python – Step 3 | 7:16 |
| Upper Confidence Bound in Python – Step 4 | 15:45 |
| Upper Confidence Bound in Python – Step 5 | 6:12 |
| Upper Confidence Bound in Python – Step 6 | 7:28 |
| Upper Confidence Bound in Python – Step 7 | 8:09 |
| Upper Confidence Bound in R – Step 1 | 13:39 |
| Upper Confidence Bound in R – Step 2 | 15:58 |
| Upper Confidence Bound in R – Step 3 | 17:37 |
| Upper Confidence Bound in R – Step 4 | 3:18 |
| Upper Confidence Bound Quiz | 5 questions |
33. Thompson Sampling – 9 lectures • 1hr 30min
| Thompson Sampling Intuition | 19:12 |
| Algorithm Comparison: UCB vs Thompson Sampling | 8:12 |
| Thompson Sampling in Python – Step 1 | 5:47 |
| Thompson Sampling in Python – Step 2 | 12:19 |
| Thompson Sampling in Python – Step 3 | 14:03 |
| Thompson Sampling in Python – Step 4 | 7:45 |
| Additional Resource for this Section | 0:28 |
| Thompson Sampling in R – Step 1 | 19:01 |
| Thompson Sampling in R – Step 2 | 3:27 |
| Thompson Sampling Quiz | 5 questions |
34. ——————– Part 7: Natural Language Processing ——————–25 lectures • 3hr 6min
| Welcome to Part 7 – Natural Language Processing | 1:05 |
| NLP Intuition | 3:02 |
| Types of Natural Language Processing | 4:11 |
| Classical vs Deep Learning Models | 11:22 |
| Bag-Of-Words Model | 17:05 |
| Natural Language Processing in Python – Step 1 | 7:13 |
| Natural Language Processing in Python – Step 2 | 6:45 |
| Natural Language Processing in Python – Step 3 | 12:54 |
| Natural Language Processing in Python – Step 4 | 11:00 |
| Natural Language Processing in Python – Step 5 | 17:24 |
| Natural Language Processing in Python – Step 6 | 9:52 |
| Natural Language Processing in Python – EXTRA | 0:23 |
| Homework Challenge | 0:43 |
| Natural Language Processing in R – Step 1 | 16:35 |
| Warning – Update | 0:22 |
| Natural Language Processing in R – Step 2 | 8:39 |
| Natural Language Processing in R – Step 3 | 6:27 |
| Natural Language Processing in R – Step 4 | 2:57 |
| Natural Language Processing in R – Step 5 | 2:05 |
| Natural Language Processing in R – Step 6 | 5:49 |
| Natural Language Processing in R – Step 7 | 3:26 |
| Natural Language Processing in R – Step 8 | 5:20 |
| Natural Language Processing in R – Step 9 | 12:50 |
| Natural Language Processing in R – Step 10 | 17:31 |
| Homework Challenge | 0:47 |
| Natural Language Processing Quiz | 5 questions |
35. ——————– Part 8: Deep Learning ——————–2 lectures • 13min
| Welcome to Part 8 – Deep Learning | 0:23 |
| What is Deep Learning? | 12:34 |
| Deep Learning Quiz | 5 questions |
36. Artificial Neural Networks – 20 lectures • 3hr 26min
| Plan of attack | 2:51 |
| The Neuron | 16:24 |
| The Activation Function | 8:29 |
| How do Neural Networks work? | 12:47 |
| How do Neural Networks learn? | 12:58 |
| Gradient Descent | 10:12 |
| Stochastic Gradient Descent | 8:44 |
| Backpropagation | 5:21 |
| Business Problem Description | 4:59 |
| ANN in Python – Step 1 | 10:21 |
| ANN in Python – Step 2 | 18:36 |
| ANN in Python – Step 3 | 14:28 |
| ANN in Python – Step 4 | 11:58 |
| ANN in Python – Step 5 | 16:25 |
| ANN in R – Step 1 | 17:17 |
| ANN in R – Step 2 | 6:30 |
| ANN in R – Step 3 | 12:29 |
| ANN in R – Step 4 (Last step) | 14:07 |
| Deep Learning Additional Content | 0:24 |
| EXTRA CONTENT: ANN Case Study | 0:14 |
| ANN QUIZ | 5 questions |
37. Convolutional Neural Networks – 16 lectures • 3hr 14min
| Plan of attack | 3:31 |
| What are convolutional neural networks? | 15:49 |
| Step 1 – Convolution Operation | 16:38 |
| Step 1(b) – ReLU Layer | 6:41 |
| Step 2 – Pooling | 14:13 |
| Step 3 – Flattening | 1:52 |
| Step 4 – Full Connection | 19:24 |
| Summary | 4:19 |
| Softmax & Cross-Entropy | 18:20 |
| CNN in Python – Step 1 | 11:35 |
| CNN in Python – Step 2 | 17:46 |
| CNN in Python – Step 3 | 17:56 |
| CNN in Python – Step 4 | 7:21 |
| CNN in Python – Step 5 | 14:55 |
| CNN in Python – FINAL DEMO! | 23:38 |
| Deep Learning Additional Content #2 | 0:21 |
| CNN Quiz | 5 questions |
38. ——————– Part 9: Dimensionality Reduction ——————–1 lecture • 1min
| Welcome to Part 9 – Dimensionality Reduction | 0:33 |
39. Principal Component Analysis (PCA) – 6 lectures • 1hr 3min
| Principal Component Analysis (PCA) Intuition | 3:49 |
| PCA in Python – Step 1 | 16:52 |
| PCA in Python – Step 2 | 5:30 |
| PCA in R – Step 1 | 12:08 |
| PCA in R – Step 2 | 11:22 |
| PCA in R – Step 3 | 13:42 |
| PCA Quiz | 5 questions |
40. Linear Discriminant Analysis (LDA) – 3 lectures • 39min
| Linear Discriminant Analysis (LDA) Intuition | 3:50 |
| LDA in Python | 14:52 |
| LDA in R | 19:59 |
| LDA Quiz | 5 questions |
41. Kernel PCA – 2 lectures • 32min
| Kernel PCA in Python | 11:03 |
| Kernel PCA in R | 20:30 |
42. ——————– Part 10: Model Selection & Boosting ——————–1 lecture • 1min
| Welcome to Part 10 – Model Selection & Boosting | 0:29 |
43. Model Selection – 6 lectures • 1hr 23min
| k-Fold Cross-Validation Intuition | 8:57 |
| Bias-Variance Tradeoff | 4:47 |
| k-Fold Cross Validation in Python | 13:45 |
| Grid Search in Python | 21:56 |
44. XGBoost – 3 lectures • 34min
| XGBoost in Python | 14:48 |
| Model Selection and Boosting Additional Content | 0:32 |
45. Annex: Logistic Regression (Long Explanation) – 1 lecture • 17min
| Logistic Regression Intuition | 17:06 |
46. Congratulations!! Don’t forget your Prize 🙂 2 lectures • 2min
| Huge Congrats for completing the challenge! | 1:31 |
| Bonus: How To UNLOCK Top Salaries (Live Training) | 0:44 |
Instructor: Kirill Eremenko
My name is Kirill Eremenko and I am super-psyched that you are reading this!
Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists.
From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics.
To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you!
Instructor: Hadelin de Ponteves
Hadelin is an online entrepreneur who has created 30+ top-rated educational e-courses to the world on new technology topics such as Artificial Intelligence, Machine Learning, Deep Learning, Blockchain and Cryptocurrencies. He is passionate about bringing this knowledge to the world and help as much people as possible. So far more than 2 million students have subscribed to his courses.
Instructor: SuperDataScience Team
Hi there,
We are the SuperDataScience team. You will hear from us when new SuperDataScience courses are released, when we publish new podcasts, blogs, share cheat sheets, and more!
We are here to help you stay on the cutting edge of Data Science and Technology.
See you in class,
Sincerely,
SuperDataScience Team!
Instructor: Ligency Team
Hi there,
We are the Ligency PR and Marketing team. You will be hearing from us when new courses are released, when we publish new podcasts, blogs, share cheatsheets and more!
We are here to help you stay on the cutting edge of Data Science and Technology.
See you in class,
Sincerely,
The Real People at Ligency
Course Feature
Course Feature
Course Provider: Udemy
UEN: N/A
Course Reference Number: N/A
Mode Of Training: Online
| FULL COURSE FEE | $139.98 |
|---|---|
| Duration | 42h 48m |
Available in: English
