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Learn Python for Data Science & Machine Learning from A-Z- Level 3

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129 STUDENTS
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Course Curriculum

1. Introduction To Python For Data Science & Machine Learning From A-Z
1.1 Who is this course for? FREE 00:03:00
1.2 Data science + machine learning marketplace FREE 00:07:00
1.3 Data science job opportunities FREE 00:04:00
1.4 Data science job roles FREE 00:10:00
1.5 What is a data scientist? FREE 00:17:00
1.6 How to get a data science job FREE 00:19:00
1.7 Data science projects overview 00:12:00
2. Data Science & Machine Learning Concepts
2.1 Why we use python 00:03:00
2.2 What is data science? 00:13:00
2.3 What is machine learning? 00:14:00
2.4 Machine learning concepts & algorithms 00:15:00
2.5 What is deep learning? 00:10:00
2.6 Machine learning vs deep learning 00:11:00
3. Python For Data Science
3.1. What is programming? 00:06:00
3.2. Why python for data science? 00:05:00
3.3. What is jupyter? 00:04:00
3.4. What is google colab? 00:03:00
3.5. Python variables, booleans 00:12:00
3.6. Getting started with google colab 00:09:00
3.7. Python operators 00:25:00
3.8. Python numbers & booleans 00:08:00
3.9. Python strings 00:13:00
3.10. Python conditional statements 00:14:00
3.11. Python for loops and while loops 00:08:00
3.12. Python lists 00:05:00
3.13. More about lists 00:15:00
3.14. Python tuples 00:11:00
3.15. Python dictionaries 00:20:00
3.16. Python sets 00:10:00
3.17. Compound data types & when to use each one? 00:13:00
3.18. Python functions 00:14:00
3.19. Object-oriented programming in python 00:19:00
4. Statistics for Data Science
4.1. Introduction to statistics 00:07:00
4.2. Descriptive statistics 00:07:00
4.3. Measure of variability 00:12:00
4.4. Measure of variability continued 00:10:00
4.5. Measures of variable relationship 00:08:00
4.6. Inferential statistics 00:15:00
4.7. Measure of asymmetry 00:02:00
4.8. Sampling distribution 00:08:00
5. Probability And Hypothesis Testing
5.1. What exactly is probability? 00:04:00
5.2. Expected values 00:03:00
5.3. Relative frequency 00:05:00
5.4. Hypothesis testing overview 00:09:00
6. NumPy Data Analysis
6.1. Intro numpy array data types 00:13:00
6.2. Numpy arrays 00:08:00
6.3. Numpy arrays basics 00:12:00
6.4. Numpy array indexing 00:09:00
6.5. Numpy array computations 00:06:00
6.6. Broadcasting 00:05:00
7. Pandas Data Analysis
7.1. Intro to pandas 00:16:00
7.2. Intro to pandas continued 00:18:00
8. Python Data Visualization
8.1. Data visualization overview 00:25:00
8.2. Different data visualization libraries in python 00:13:00
8.3. Python data visualization implementation 00:08:00
9. Introduction To Machine Learning
9.1. Introduction to machine learning 00:26:00
10. Data Loading & Exploration
10.1. Exploratory data analysis 00:13:00
11. Data Cleaning
11.1. Feature scaling 00:08:00
11.2. Data cleaning 00:08:00
12. Feature Selecting And Engineering
12.1. Feature engineering 00:06:00
13. Linear And Logistic Regression
13.1. Linear regression Intro 00:08:00
13.2. Gradient descent 00:06:00
13.3. Linear regression + correlation methods 00:27:00
13.4. Linear regression Implementation 00:05:00
13.5. Logistic regression 00:03:00
14. K Nearest Neighbors
14.1. Parametric vs non-parametric models 00:03:00
14.2. Eda on iris dataset 00:22:00
14.3. The knn intuition 00:02:00
14.4. Implement the knn algorithm from scratch 00:12:00
14.5. Compare the result with the sklearn library 00:04:00
14.6. Hyperparameter tuning using the cross-validation 00:11:00
14.7. The decision boundary visualization 00:05:00
14.8. Manhattan vs euclidean distance 00:11:00
14.9. Feature scaling in knn 00:06:00
14.10. Curse of dimensionality 00:08:00
14.11. Knn use cases 00:04:00
14.12. Knn pros and cons 00:06:00
15. Decision Trees
15.1. Decision Trees Section Overview 00:04:00
15.2. EDA on Adult Dataset 00:17:00
15.3. What is Entropy and Information Gain? 00:22:00
15.4. The Decision Tree ID3 algorithm from scratch Part 1 00:12:00
15.5. The Decision Tree ID3 algorithm from scratch Part 2 00:08:00
15.6. The Decision Tree ID3 algorithm from scratch Part 3 00:04:00
15.7. ID3 – Putting Everything Together 00:21:00
15.8. Evaluating our ID3 implementation 00:17:00
15.9. Compare with Sklearn implementation 00:09:00
15.10. Visualizing the tree 00:10:00
15.11. Plot the Important Features 00:06:00
15.12. Decision Trees Hyper-parameters 00:12:00
15.13. Pruning 00:17:00
15.14. [Optional] Gain Ration 00:03:00
15.15. Decision Trees Pros and Cons 00:08:00
15.16. [Project] Predict whether income exceeds $50K/yr – Overview 00:03:00
16. Ensemble Learning And Random Forests
16.1. Ensemble Learning Section Overview 00:04:00
16.2. What is Ensemble Learning? 00:13:00
16.3. What is Bootstrap Sampling? 00:08:00
16.4. What is Bagging? 00:05:00
16.5. Out-of-Bag Error (OOB Error) 00:08:00
16.6. Implementing Random Forests from scratch Part 1 00:23:00
16.7. Implementing Random Forests from scratch Part 2 00:06:00
16.8. Compare with sklearn implementation 00:04:00
16.9. Random Forests Hyper-Parameters 00:04:00
16.10. Random Forests Pros and Cons 00:05:00
16.11. What is Boosting? 00:05:00
16.12. AdaBoost Part 1 00:04:00
16.13. AdaBoost Part 2 00:15:00
17. Support Vector Machines
17.1. SVM Outline 00:05:00
17.2. SVM intuition 00:12:00
17.3. Hard vs Soft Margins 00:13:00
17.4. C hyper-parameter 00:04:00
17.5. Kernel Trick 00:12:00
17.6. SVM – Kernel Types 00:18:00
17.7. SVM with Linear Dataset (Iris) 00:14:00
17.8. SVM with Non-linear Dataset 00:13:00
17.9. SVM with Regression 00:06:00
17.10. [Project] Voice Gender Recognition using SVM 00:04:00
18. K-Means
18.1 Unsupervised Machine Learning Introduction 00:20:00
18.2 Unsupervised Machine Learning Continued 00:20:00
18.3 Data Standardization 00:19:00
19. PCA
19.1. PCA Section Overview 00:05:00
19.2. What is PCA? 00:10:00
19.3. PCA Drawbacks 00:04:00
19.4. PCA Algorithm Steps (Mathematics) 00:13:00
19.5. Covariance Matrix vs SVD 00:05:00
19.6. PCA – Main Applications 00:03:00
19.7. PCA – Image Compression 00:27:00
19.8. PCA Data Preprocessing 00:15:00
19.9. PCA – Biplot and the Screen Plot 00:17:00
19.10. PCA – Feature Scaling and Screen Plot 00:09:00
19.11. PCA – Supervised vs Unsupervised 00:05:00
19.12. PCA – Visualization 00:08:00
20. Data Science Career
20.1. 1.Creating A Data Science Resume 00:07:00
20.2. Data Science Cover Letter 00:04:00
20.3. How to Contact Recruiters 00:04:00
20.4. Getting Started with Freelancing 00:04:00
20.5. Top Freelance Websites 00:06:00
20.6. Personal Branding 00:04:00
20.7. Networking 00:04:00
20.8. Importance of a Website 00:03:00
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Students feedback

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Avarage rating (2)
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    H W

    Harper Wood

    August 19, 2021
    Learning journey

    This course made my learning journey interesting.

    A B

    Ashton Bailey

    August 14, 2021
    Always Helpful

    Your team is always so nice and amazing all throughout the learning

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