Full Stack Data Science & Machine Learning BootCamp Course

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Akhil Vydyula

34:27:12

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1. Introduction to the Full Stack Data Science Course

1. Introduction to the Full Stack Data Science Course.mp4

00:32

2. PYTHON - Introduction to Basics of Python for Beginners

1.1 agent_classification.csv

1.2 data structure - list,list project and tuple.zip

1.3 data structure - sets.zip

1.4 data structure- dictionary.zip

1.5 datetime features.zip

1.6 No2 dataset.csv

1.7 string manipulation.zip

1. Python - Data Structures (Lists, Tuple, Dictionary) and String Manipulations.mp4

51:51

2.1 implementing functions in python.zip

2.2 implementing recursion in python.zip

2.3 kpi for classification.zip

2.4 lambda expressions.zip

2. Python - Implementation Of Lambda, Recursion, Functions..mp4

50:47

3.1 advertisement predictions using logistic regression.zip

3.2 advertising.csv

3.3 eda - part1.zip

3.4 eda - part2.zip

3.5 eda- descriptive analysis.zip

3.6 standard libraries in python.zip

3. Python - Understand Of Libraries,Exploratory Data Analysis,Descriptive Analysis.mp4

44:38

3. Business Statistics for Data Analysis

1.1 tutorial_stats_pel.zip

1. Introduction to statistics and Measures of central tendencies.mp4

15:41

2.1 data_cleaning_insights.zip

2.2 data.csv

2. Central Limit Theorem - CLT.mp4

30:18

3. Distributions and Correlations.mp4

51:02

4. PDF & CDF and Hypothesis Testing.mp4

40:34

5.1 basics_of_time_series_forecasting.zip

5.2 Time_Series_AirPassengers.csv

5. Time Series Analysis & Forecasting.mp4

52:43

6.1 tutorial_probability.zip

6.2 tutorial_statistics.zip

6. Probability Theory and Statistical Analysis.mp4

01:09:37

7.1 project_5_uk_road_accident_timeseries_forecasting.zip

7. Capstone Project - UK Road Accident Analysis Part - 1.mp4

32:20

8.1 uk_road_accident_analytics.zip

8. Capstone Project - UK Road Accident Part -2.mp4

28:54

4. MACHINE LEARNING

1.1 churn prediction logistic regression.zip

1.2 churn_prediction.csv

1.3 data_cleaned.csv

1.4 logistic regression notebook.zip

1. MACHINE LEARNING - Classical Machine Learning Algorithm Logistic Regression.mp4

59:00

2.1 bag of words.zip

2.2 covid_19_india.csv

2.3 IndividualDetails.csv

2.4 ISPA.xlsx

2.5 novel corona virus data analysis - india.zip

2.6 tf-idf.zip

2.7 word2vec.zip

2. MACHINE LEARNING - Word Embedding Techniques BoW, TF -IDF, W2V etc.mp4

45:32

3. MACHINE LEARNING - Text Cleaning and Preprocessing with Amazon Reviews Data.mp4

01:05:19

4. MACHINE LEARNING - Classical Machine Learning Algorithm Linear Regression.mp4

47:27

5. MACHINE LEARNING - Decision Tree Classifier and Regression with Example.mp4

43:59

6. MACHINE LEARNING - Geometric Intuition of Ensembles Models and Flask Project.mp4

01:05:56

7. MACHINE LEARNING - Data Analysis on Loan Approval Status.mp4

01:13:05

8. MACHINE LEARNING - Unsupervised Learning Algorithms K means Cluster Techniques.mp4

51:59

5. ML Capstone Project 1 Flight_Fare_Prediction

1.1 app.zip

1.2 Data_Train.xlsx

1.3 flight_rf.zip

1.4 home.html

1.5 project_1_flight_fare_price_prediction.zip

1.6 styles.zip

1.7 Test_set.xlsx

1. Project_1 - Flight_Fare_Prediction.mp4

10:20

2. Feature Engineering and Applying Classical ML Models.mp4

23:36

3. Deploy the Model with Flask Framework.mp4

05:12

6. ML Capstone Project 2 Mushroom_Classification

1.1 mushrooms.csv

1.2 project_2_mushroom_classification.zip

1. Project_2_Mushroom_Classification - Exploratory Data Analysis.mp4

16:56

2. Building The Benchmark Model and Evaluation.mp4

14:59

7. ML Capstone Project 3 NurserySchool_Application_Classification

1.1 nursery_data.csv

1.2 project_3_nursery_school_system.zip

1. Project_3_NurserySchool_Application_Classification.mp4

12:39

2. Logistic Regression, SVM, Decision Tree Models & Evaluation Metrics.mp4

09:50

8. ML Capstone Project 4 Toxic_Comments_Classification

1.1 project_4_toxic_comments_classification_eda.zip

1.2 project_4_toxic_comments_classification.zip

1. Project_4_Toxic_Comments_Classification.mp4

16:47

2. NLP - Tokenized Sequences for Visualization.mp4

04:48

3. Model Refinement - Optimize NB,SVM,LR with Feature Weight.mp4

16:08

9. ML Capstone Project 5 UK_Road_Accident_Timeseries_Forecasting

1.1 project_5_uk_road_accident_timeseries_forecasting.zip

1. Project_5_UK_Road_Accident_Timeseries_Forecasting_EDA.mp4

32:20

2. Forecast UK Accident rates based on Number of Casualties on SARIMA,FbP,LSTMs.mp4

28:54

10. Structured Query Language (SQL)

1. Introduction to SQL - SQL Syntax and Download MySQL.mp4

08:17

2. RDBMS - Data Integrity, Database Normalization.mp4

22:15

3. Data Definition Language (DDL).mp4

22:15

4. Data Manipulation language (DML).mp4

19:32

5. Data Control Languages (DCL) and Domain Constraints.mp4

23:10

6. Filtering Data and SET Operators in SQL.mp4

21:31

7. Conditional Expressions in SQL.mp4

23:45

8. Grouping Data.mp4

29:21

9. Joining Multiple Tables (JOINS).mp4

16:43

10. SQL RANK Functions.mp4

18:47

11. SQL Triggers and Stored Procedures.mp4

31:16

12. SQL Capstone Project 1 Data Analytics on Movie Reviews in SQL.mp4

38:27

11. DEEP LEARNING

1.1 mlp.zip

1. DEEP LEARNING - Introduction to Neural Networks and Basics of MLP, BACKPROP.mp4

51:33

2.1 lstm_implementation_from_scratch.zip

2.2 rnn_classification.zip

2. DEEP LEARNING - In Depth Understanding of RNN and LSTM with Examples.mp4

49:05

3.1 cnn.zip

3. DEEP LEARNING - Intuition Behind the Computer Vision and CNN Algorithm.mp4

56:08

4.1 cnn_on_cifr project.zip

4. DEEP LEARNING - Convolutional Neural Networks with Pizza and CIFAR Projects.mp4

46:59

5. DEEP LEARNING - Practical Examples on Transfer Learning for Vgg16 Model.mp4

50:31

6.1 app.zip

6.2 base.html

6.3 index.html

6.4 main.zip

6.5 requirements.zip

6.6 util.zip

6. DEEP LEARNING - Web Based Flask Framework for Wild Animal Recognition with CNN.mp4

26:55

12. Microsoft Excel

1.1 Data-Formatting-Tools.xlsx

1.2 Lesson-4-Working-with-Cells-and-Ranges.xlsx

1. Introduction to Excel Workbook.mp4

50:38

2.1 Lesson-6-Excel-Tables.xlsx

2.2 Lesson-7-AutoFill-Custom-Fill-and-Flash-Fill.xlsx

2. Hands on Excel Cells and Ranges.mp4

19:43

3.1 Lesson-9-Excel-Formula-basics.xlsx

3.2 Logical-Formulas.xlsx

3. Basic Formulae - Logical Operators.mp4

17:01

4.1 Lesson-11-Math-Formulas.xlsx

4.2 Lookup-and-Reference-Formulas.xlsx

4. Excel - Lookup and Reference Formulae.mp4

23:29

5.1 Stat-Formulas.xlsx

5.2 Text-Formulas.xlsx

5. Excel - Logical Formulae.mp4

11:33

6.1 Lesson-15-Date-and-Time-Formulas.xlsx

6.2 Lesson-16-Formula-Mix-and-Match.xlsx

6. Text and Statistical Formulae.mp4

26:40

7.1 Data-Sorting-and-Filtering.xlsx

7.2 Lesson-19-Data-Sorting-and-Filtering.xlsx

7. Excel - Date & Time Formulae.mp4

15:34

8.1 Lesson-23-Dynamic-Charting-Example.xlsx

8. Excel - Sorting & Filtering.mp4

16:52

9.1 Lesson-24-Pivot-Table.xlsx

9. Dynamic Charts With Examples.mp4

11:54

10. Derive Insights with Pivot Tables.mp4

11:20

13. Microsoft Power BI (Business Intelligence Tool)

1.1 basic charts.zip

1.2 Sample - Superstore.xlsx

1. Installation Power BI Desktop and Applications of Power BI.mp4

26:27

2.1 3.4 State and UTs of India.xlsx

2.2 maps.zip

2.3 Sample - Superstore.xlsx

2. Understand the Concepts of Maps using Power BI.mp4

14:11

3.1 cards and filters.zip

3.2 Sample - Superstore.xlsx

3.3 section 5 - other charts.zip

3.4 table and matrix.zip

3. Power BI - Tables and Matrix.mp4

27:31

4.1 reference.zip

4.2 Sample - Superstore.xlsx

4.3 sample dashboard.zip

4.4 slicers.zip

4. Different Types of Power BI Slicers.mp4

22:26

5.1 reference.zip

5.2 Sample - Superstore.xlsx

5.3 slicers.zip

5. Introduction to Power Query.mp4

21:49

6.1 publishing reports to power bi service.zip

6. Hands on with Power Query Operations.mp4

19:05

7.1 12. Power Query - Date Functions.xlsx

7.2 section 12 - pq date functions.zip

7. Manipulations with Power Query Operations.mp4

14:52

8.1 13. Power Query - Number Functions.xlsx

8.2 publishing reports to power bi service.zip

8. Build a Super Store Sales Dashboard.mp4

24:17

9.1 13. Power Query - Number Functions.xlsx

9.2 14.5 append different data sources in power bi.zip

9.3 sales and production analysis new.zip

9. BI Capstone Project - Sales and Production Analysis.mp4

21:37

Description

Learn Python, Excel,Deep Learning, Power BI, SQL, Artificial Intelligence,Business Statistics, Capstone Projects & more!

What You'll Learn?

Build a portfolio of data science projects to apply for jobs in the industry
Learn how to create pie, bar, line, area, histogram, scatter, regression, and combo charts
Create your own neural networks and understand how to use them to perform deep learning
Understand and apply data visualisation techniques to explore large datasets
Use data science algorithms to analyse data in real life projects such as Mushroom classification and image recognition
Understand how to use the latest tools in data science, including Tensorflow, Matplotlib, Numpy and many more

Who is this for?

If you want to take ONE COURSE and learn everything you need to know about data science and machine learning then take this course.

If you are a seasoned programmer, take this course to get up to speed quickly with the workflow of a data scientist.

If you want to learn how to build machine learning algorithms such as deep learning and neural networks.

The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills

More details

Description
Welcome to the Full Stack Data Science & Machine Learning BootCamp Course, the only course you need to learn Foundation skills and get into data science.

At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here's why:
The course is taught by the lead instructor at the PwC, India's leading in-person programming bootcamp.
In the course, you'll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix.
This course doesn't cut any corners, there are beautiful animated explanation videos and real-world projects to build.
The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback.
To date, Iâ€™ve taught over 10000+ students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup.
You'll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp.

We'll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional.

The course includes over 40+ hours of HD video tutorials and builds your programming knowledge while solving real-world problems.

In the curriculum, we cover a large number of important data science and machine learning topics, such as:
MACHINE LEARNING -
Regression: Simple Linear Regression, , SVR, Decision Tree , Random Forest,
Clustering: K-Means, Hierarchical Clustering Algorithms
Classification: Logistic Regression, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
Natural Language Processing: Bag-of-words model and algorithms for NLP

DEEP LEARNING -
Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long short term Memory, Vgg16 , Transfer learning, Web Based Flask Application.
Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

By the end of this course, you will be fluently programming in Python and be ready to tackle any data science project. Weâ€™ll be covering all of these Python programming concepts:

PYTHON -
Data Types and Variables
String Manipulation
Functions
Objects
Lists, Tuples and Dictionaries
Loops and Iterators
Conditionals and Control Flow
Generator Functions
Context Managers and Name Scoping
Error Handling

Power BI -
What is Power BI and why you should be using it.
To import CSV and Excel files into Power BI Desktop.
How to use Merge Queries to fetch data from other queries.
How to create relationships between the different tables of the data model.
All about DAX including using the COUTROWS, CALCULATE, and SAMEPERIODLASTYEAR functions.
All about using the card visual to create summary information.
How to use other visuals such as clustered column charts, maps, and trend graphs.
How to use Slicers to filter your reports.
How to use themes to format your reports quickly and consistently.
How to edit the interactions between your visualizations and filter at visualization, page, and report level.
By working through real-world projects you get to understand the entire workflow of a data scientist which is incredibly valuable to a potential employer.

Sign up today, and look forward to:
178+ HD Video Lectures
30+ Code Challenges and Exercises
Fully Fledged Data Science and Machine Learning Projects
Programming Resources and Cheatsheets
Our best selling 12 Rules to Learn to Code eBook
$12,000+ data science & machine learning bootcamp course materials and curriculum
Who this course is for:
If you want to take ONE COURSE and learn everything you need to know about data science and machine learning then take this course.
If you are a seasoned programmer, take this course to get up to speed quickly with the workflow of a data scientist.
If you want to learn how to build machine learning algorithms such as deep learning and neural networks.
The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills

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Hi There!My Name is Akhil Vydyula, I am a Data Scientist I was previously worked on BFSI data analysis and modelling skills to oversee the full-life cycle of development and execution. He possess strong.ability to data wrangling, feature engineering, algorithm development, model training and implementation.SKILLS AND COMPETENCIESExpert knowledge and experience with C/C++/python Programming and SQL.Should be able to learn and Implement new technologies quickly and effectively.Excellent Mathematical Skills, Problem Solving & Logical Skills.Actively Participating in hackathons in various platforms and writing blogs in medium.TECHNICAL SKILLSMachine Learning, Natural Language Processing(NLP),Computer Vision,Regression, Multi LabelClassification.Transfer Learning, Transformers, Ensembles, Stacking Classifiers.AutoML, SQL, Python, Keras, Pandas, NumPy, Seaborn,Matplotlib,Clustering,Recommendation Systems,Time Series Analysis.

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