Python for Data Science & Machine Learning: Zero to Hero

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1. Introduction

1. Welcome to the Python for Data Science & ML bootcamp!.mp4

00:36

2. Python A Brief Overview.mp4

00:52

3. The Python Installation Procedure.mp4

02:24

4. What Jupyter is.mp4

00:59

5. Set up Anaconda on Different Operating Systems.mp4

04:15

6. How to integrate Python into Jupyter.mp4

00:44

7. Handling Directories in Jupyter Notebook.mp4

02:48

8. Input & Output.mp4

01:44

9. Working with different datatypes.mp4

01:06

10. Variables.mp4

01:50

11. Arithmetic Operators.mp4

01:48

12. Comparison Operators.mp4

00:43

13. Logical Operators.mp4

03:05

14. Conditional statements.mp4

02:20

15. Loops.mp4

04:30

16. Sequences Part 1 Lists.mp4

03:18

17. Sequences Part 2 Dictionaries.mp4

02:48

18. Sequences Part 3 Tuples.mp4

01:07

19. Functions Part 1 Built-in Functions.mp4

00:26

20. Functions Part 2 User-defined Functions.mp4

03:14

2. The Must-Have Python Data Science Libraries

1. Completing Library Setup.mp4

00:36

2. Library Importing.mp4

01:47

3. Pandas A Data Science Library.mp4

00:48

4. NumPy A Data Science Library.mp4

00:51

5. NumPy vs. Pandas.mp4

00:33

6. Matplotlib Library for Data Science.mp4

00:37

7. Seaborn Library for Data Science.mp4

00:20

3. NumPy Mastery Everything you need to know about NumPy

1. Intro to NumPy arrays.mp4

00:45

2. Creating NumPy arrays.mp4

06:13

3. Indexing NumPy arrays.mp4

05:45

4. Array shape.mp4

00:35

5. Iterating Over NumPy Arrays.mp4

04:57

6. Basic NumPy arrays zeros().mp4

01:33

7. Basic NumPy arrays ones().mp4

01:09

8. Basic NumPy arrays full().mp4

01:16

9. Adding a scalar.mp4

01:41

10. Subtracting a scalar.mp4

01:04

11. Multiplying by a scalar.mp4

01:20

12. Dividing by a scalar.mp4

01:25

13. Raise to a power.mp4

00:48

14. Transpose.mp4

00:48

15. Element-wise addition.mp4

01:59

16. Element-wise subtraction.mp4

00:56

17. Element-wise multiplication.mp4

00:58

18. Element-wise division.mp4

01:04

19. Matrix multiplication.mp4

01:34

20. Statistics.mp4

02:54

4. DataFrames and Series in Python's Pandas

1. What is a Python Pandas DataFrame.mp4

00:57

2. What is a Python Pandas Series.mp4

00:42

3. DataFrame vs Series.mp4

00:28

4. Creating a DataFrame using lists.mp4

03:17

5. Creating a DataFrame using a dictionary.mp4

01:06

6. Loading CSV data into python.mp4

01:52

7. Changing the Index Column.mp4

01:06

8. Inplace.mp4

01:20

9. Examining the DataFrame Head & Tail.mp4

00:36

10. Statistical summary of the DataFrame.mp4

00:37

11. Slicing rows using bracket operators.mp4

01:26

12. Indexing columns using bracket operators.mp4

00:51

13. Boolean list.mp4

01:15

14. Filtering Rows.mp4

01:22

15. Filtering rows using & and operators.mp4

01:51

16. Filtering data using loc().mp4

03:35

17. Filtering data using iloc().mp4

02:23

18. Adding and deleting rows and columns.mp4

02:41

19. Sorting Values.mp4

01:39

20. Exporting and saving pandas DataFrames.mp4

01:30

21. Concatenating DataFrames.mp4

00:59

22. groupby().mp4

02:39

5. Data Cleaning Techniques for Better Data

1. Introduction to Data Cleaning.mp4

00:37

2. Quality of Data.mp4

00:47

3. Examples of Anomalies.mp4

01:04

4. Median-based Anomaly Detection.mp4

02:41

5. Mean-based anomaly detection.mp4

02:50

6. Z-score-based Anomaly Detection.mp4

02:50

7. Interquartile Range for Anomaly Detection.mp4

04:33

8. Dealing with missing values.mp4

06:01

9. Regular Expressions.mp4

06:57

10. Feature Scaling.mp4

03:17

6. Exploratory Data Analysis in Python

1. Introduction.mp4

00:19

2. What is Exploratory Data Analysis.mp4

00:30

3. Univariate Analysis.mp4

01:41

4. Univariate Analysis Continuous Data.mp4

06:00

5. Univariate Analysis Categorical Data.mp4

02:16

6. Bivariate analysis Continuous & Continuous.mp4

04:32

7. Bivariate analysis Categorical & Categorical.mp4

03:07

8. Bivariate analysis Continuous & Categorical.mp4

01:51

9. Detecting Outliers.mp4

05:34

10. Categorical Variable Transformation.mp4

04:22

7. Python for Time-Series Analysis A Primer

1. Introduction to Time Series.mp4

02:15

2. Getting stock data using yfinance.mp4

03:14

3. Converting a Dataset into Time Series.mp4

04:23

4. Working with Time Series.mp4

03:49

5. Time Series Data Visualization with Python.mp4

03:03

8. Python for Data Visualization Library Resources, and Sample Graphs

1. Introduction.mp4

00:29

2. Setting Up Matplotlib.mp4

00:33

3. Plotting Line Plots using Matplotlib.mp4

01:45

4. Title, Labels & Legend.mp4

06:46

5. Plotting Histograms.mp4

01:22

6. Plotting Bar Charts.mp4

02:04

7. Plotting Pie Charts.mp4

02:49

8. Plotting Scatter Plots.mp4

05:43

9. Plotting Log Plots.mp4

00:41

10. Plotting Polar Plots.mp4

02:06

11. Handling Dates.mp4

00:43

12. Creating multiple subplots in one figure.mp4

03:28

9. The Basics of Machine Learning

1. Why do we need machine learning.mp4

01:53

2. Machine Learning Use Cases.mp4

01:48

3. Approaches to Machine Learning.mp4

00:29

4. What is Supervised learning.mp4

01:18

5. What is Unsupervised learning.mp4

00:59

6. Supervised learning vs Unsupervised learning.mp4

03:36

10. Simple Linear Regression with Python

1. Introduction to regression.mp4

01:41

2. How Does Linear Regression Work.mp4

01:36

3. Line representation.mp4

00:58

4. Implementation in python Importing libraries & datasets.mp4

01:48

5. Implementation in python Distribution of the data.mp4

02:19

6. Implementation in python Creating a linear regression object.mp4

02:56

11. Multiple Linear Regression with Python

1. Understanding Multiple linear regression.mp4

01:34

2. Exploring the dataset.mp4

03:53

3. Encoding Categorical Data.mp4

04:47

4. Splitting data into Train and Test Sets.mp4

01:48

5. Training the model on the Training set.mp4

01:23

6. Predicting the Test Set results.mp4

02:59

7. Evaluating the performance of the regression model.mp4

01:20

8. Root Mean Squared Error in Python.mp4

02:30

12. Classification Algorithms K-Nearest Neighbors

1. Introduction to classification.mp4

01:05

2. K-Nearest Neighbors algorithm.mp4

00:55

3. Example of KNN.mp4

00:30

4. K-Nearest Neighbours (KNN) using python.mp4

01:15

5. Importing required libraries.mp4

00:51

6. Importing the dataset.mp4

01:35

7. Splitting data into Train and Test Sets.mp4

03:16

8. Feature Scaling.mp4

00:26

9. Importing the KNN classifier.mp4

02:05

10. Results prediction & Confusion matrix.mp4

01:32

13. Classification Algorithms Decision Tree

1. Introduction to decision trees.mp4

01:23

2. What is Entropy.mp4

01:17

3. Exploring the dataset.mp4

00:36

4. Decision tree structure.mp4

01:16

5. Importing libraries & datasets.mp4

00:48

6. Encoding Categorical Data.mp4

02:50

7. Splitting data into Train and Test Sets.mp4

01:06

8. Results Prediction & Accuracy.mp4

02:37

14. Classification Algorithms Logistic regression

1. Introduction.mp4

01:25

2. Implementation steps.mp4

00:52

3. Importing libraries & datasets.mp4

02:01

4. Splitting data into Train and Test Sets.mp4

01:29

5. Pre-processing.mp4

02:00

6. Training the model.mp4

01:05

7. Results prediction & Confusion matrix.mp4

02:23

8. Logistic Regression vs Linear Regression.mp4

02:26

15. Clustering

1. Introduction to clustering.mp4

00:53

2. Use cases.mp4

00:59

3. K-Means Clustering Algorithm.mp4

01:26

4. Elbow method.mp4

01:35

5. Steps of the Elbow method.mp4

01:11

6. Implementation in python.mp4

04:15

7. Hierarchical clustering.mp4

01:17

8. Density-based clustering.mp4

01:35

9. Implementation of k-means clustering in python.mp4

01:03

10. Importing the dataset.mp4

03:05

11. Visualizing the dataset.mp4

02:20

12. Defining the classifier.mp4

01:37

13. 3D Visualization of the clusters.mp4

01:19

14. 3D Visualization of the predicted values.mp4

02:51

15. Number of predicted clusters.mp4

02:03

16. Recommender System

1. Introduction.mp4

01:28

2. Collaborative Filtering in Recommender Systems.mp4

00:42

3. Content-based Recommender System.mp4

00:51

4. Importing libraries & datasets.mp4

02:57

5. Merging datasets into one dataframe.mp4

00:53

6. Sorting by title and rating.mp4

03:40

7. Histogram showing number of ratings.mp4

00:50

8. Frequency distribution.mp4

01:03

9. Jointplot of the ratings and number of ratings.mp4

01:17

10. Data pre-processing.mp4

02:04

11. Sorting the most-rated movies.mp4

01:00

12. Grabbing the ratings for two movies.mp4

01:25

13. Correlation between the most-rated movies.mp4

02:15

14. Sorting the data by correlation.mp4

00:53

15. Filtering out movies.mp4

00:41

16. Sorting values.mp4

01:02

17. Repeating the process for another movie.mp4

02:23

17. Conclusion

1. Conclusion.mp4

00:22

Description

Master Data Science & Machine Learning in Python: Numpy, Pandas, Matplotlib, Scikit-Learn, Machine Learning, and more!

What You'll Learn?

Gain familiarity with Pandas, a data analysis tool
Get a grasp on the theory behind basic and multiple linear regression
Tackle regression problems easily
Discover the logic behind decision trees
Acquaint yourself with the various clustering algorithms

Who is this for?

Aspiring Machine Learning Professionals

Anyone interested in expanding their skill set with machine learning and Python

Inquisitive technologists interested in seeing Machine Learning in action

Those who are already proficient in programming and want to expand their capabilities by learning about machine learning

What You Need to Know?

The ability to do simple math

No programming experience needed

No prior data science knowledge required

Readiness, flexibility, and passion for learning

More details

Description
This machine learning course will provide you the fundamentals of how companies like Google, Amazon, and even Udemy utilize machine learning and artificial intelligence (AI) to glean meaning and insights from massive data sets. Glassdoor and Indeed both report that the average salary for a data scientist is $120,000. This is the standard, not the exception.
Data scientists are already quite desirable. It's difficult to keep them on staff in today's tight labor market. There is a severe shortage of people who possess the rare combination of scientific training, computer expertise, and analytical talents.
Today's data scientists are held to the same standards as the Wall Street "quants" of the '80s and '90s. When the need arose for innovative algorithms and data approaches, physicists and mathematicians flocked to investment banks and hedge funds.
So, it's no surprise that data science is rising to prominence as a promising career path in the modern day. It is analytic in focus, driven by code, and performed on a computer. As a result, it shouldn't be a shock that the demand for data scientists has been growing steadily in the workplace for the past few years.
On the other hand, availability has been low. Obtaining the education and experience necessary to be hired as a data scientist is tough. And that's why we made this course in the first place!
Each topic is described in plain English, and the course does its best to avoid mathematical notations and jargon. Once you have access to the source code, you can experiment with it and improve upon it. Learning and applying these algorithms in the real world, rather than in a theoretical or academic setting, is the focus of this course.
Each video will leave you with a new perspective that you can implement right away!
If you have no background in statistics, don't let that stop you from enrolling in this course; we welcome students of all levels.
Who this course is for:
Aspiring Machine Learning Professionals
Anyone interested in expanding their skill set with machine learning and Python
Inquisitive technologists interested in seeing Machine Learning in action
Those who are already proficient in programming and want to expand their capabilities by learning about machine learning

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