3 Course Bundle
Data Science
Machine Learning
Complete Python
BEGINNER TO ADVANCE
₹2,500 ₹498/-
80% Off! Limited Time
20hrs Video Content
+
50+ Modules
+
Audiobooks
+
Practical Notes
+
Practical Projects
+
Interview lessons & questions
- No Prior Programming Knowledge Required
Why Buy Now?
- ONE TIME OFFER
- 80% off for Limited Time
- Practical Video Explaination
- Illustrative Diagrams to help you understand concepts better
- Easy to Understand language
- Detailed Explanation
- Updated Content
- Complete Range from Beginner to Advance
- Practical Questions for Interview purpose
We help you to complete the entire course, by
guiding you every single day... till the end of the course
You may ask...
EXCLUSIVE CONTENT
How are these Books different than other Books?
Cheaper than any other Courses
Detailed Explanation
Exam/Interview Oriented
Easy to Understand Language
Practical Examples
Available in all formats
Why other courses didn't work, & this will work for you...
Develop Confidence in
DS, ML & Python
How is it helpful in my career?
Basic to Advance
No matter what you know, this course teaches you from basic terms to advance level.
All Concepts Covered
With this course you learn each and every aspect of all.
Self Sufficient
After this course, you'll have complete clarity on all subjects.
Global Opportunity
With global rise in demand, opportunities are endless in terms of jobs.
What others have to say about it..
What exactly do I get in this?
Data Science
- Introduction
- What is Big Data and for what reason does it make a difference?
- Applications of big data across industry areas
- Data researcher
- The primary innovative parts in a Big Data environment
- Summary
- Business analytics
- Incorporating Data-Driven Insights into the Business Process
- Deploying analytics and data wrangling to convert raw data into actionable insights
- Distinguishing Business Intelligence and Data Science
- Knowing Who to Call to Get the Job Done Right
- Data analytics
- Data
- Subjective data
- Data Strategies
- Imagining Data
- The "Issue" with Data Analysis
- Data Analysis as a Linear Process
- Data Analysis as a Cycle
- Data science
- What is data science?
- Where data comes from
- Working with data at scale
- Techniques for working with Traditional Data
- Introduction
- Traditional data storage and processing methods
- Application and performance analysis
- Conclusions
- Business Intelligence (BI) Techniques
- Introduction
- Business Intelligence
- Parts of BI
- Issues In Bi
- Eventual fate of business Insight
- Purposes behind business knowledge
- Advantages of BI
- Business Intelligence Technology
- Conclusion
- Machine Learning (ML) Techniques
- Supervised Learning Approach
- Unsupervised learning
- Algorithm Types
- Necessary Programming Languages and Software Used in Data Science
- Introduction
- Popularity
- Library Support
- Community
- OS apparatus recognizable proof
- Proposed grouping plan qualities
- Proposed grouping plan
- Conclusion
- Probability, Frequency, Events and Their Complements
- Probability
- Bayes' Theorem
- Descriptive Statistics
- Proportions of Center: Mean, Median, Mode, Mid-range
- Proportions of Variability: Range, Variance, Standard Deviation
- Proportions of Position: Percentile, Z-score, Quartiles
- Conclusion
- Permutations and How to Use Them
- Introduction
- History
- Notation
- Different employments of the term permutation
- Symmetry of Combination
- Introduction
- Neural Machine Translation
- Neural System Combination for Machine Translation
- Experiments
- Related Work
- Conclusion and Future Work
- Sets and Events, Bayes' Law
- Introduction
- Sets and Events
- Methodology
- A Counting Heuristic
- Relating the Counting Heuristic to Bayes' Rule
- Further Reading
- Conclusion
- Probability Distributions
- General Properties of Probability Distributions
- Discrete Probability Distributions
- Kinds of Discrete Distribution
- Illustration of How to Use Discrete Probability Distributions
- Constant Probability Distributions
- Attributes of Continuous Probability Distributions
- Illustration of Using the Normal Probability Distribution
- Illustration of Using the Lognormal Probability Distribution
- Theory Testing Uses Special Probability Distributions
- Conclusion
- Types of Data, Measurement, Histogram, Mean, Median and Mode, Skewness, Variance, Covariance
- Trendy expressions
- Types of data
- Types of Data Science
- Histogram, Skewness, Mean, Median, and Mode
- Variance versus Covariance: An Overview
- Conclusions
- Distribution, Normal Distribution, Standard Normal Distribution, Central Limit Theorem, Standard error, Estimators and Estimates
- Introduction
- Distribution
- Understanding Central Limit Theorem
- What is an Estimate and Estimator?
- Confidence Intervals, Student's T Distribution, Margin of Error
- Introduction
- What is T Distribution?
- What is a Margin of Error?
- Null vs Alternative Hypothesis, Rejection Region, and Significance Level Type I Error and Type II Error, Test for the Mean. Population Variance Known, p-value
- Introduction
- Hypothesis Testing: Upper-, Lower, and Two-Tailed Tests
- Type I and Type II Errors
- Tests with One Sample, Continuous Outcome
- Conclusion
- Python
- Presenting data science and Python
- Scientific distributions
- A look at the fundamental bundles
- Downloading the model code
- Presenting Jupyter
- Data Munging
- Data preprocessing
- Data determination
- Working with categorical and text data
- Controlling the memory size
- Summary
- Deep learning
- Introduction
- General Theory of Data Science
- Theory of Data Science Process
- General Theory of Artificial Intelligence
- Theory of Data
- Data Extract, Transformation and Loading (ETL)
- Theory of Deep Learning
- Deep Learning Algorithms Cheat Sheet
- and much more..
Video Lessons Includes:
- Python
- Introduction to python
- Datatypes, Variables, Strings
- Loops, List, Functions, Tuples, Dictionaries
- Assignments on python, Interview questions on python, Use of Python
- Data Analysis
- Understanding the basic libraries required for data analysis and data science in detail
- How to import dataset
- Data preprocessing
- Data Visualization and Exploratory
- Data Analysis using 7-8 different graphs and plots
- Interview questions, mini project and use of data analysis in data science and career data analysis
- Machine Learning Algorithms for Data Science
- KNN Classification
- Decision tree classification
- Random forest classification
- Linear regression
- Interview questions on data science, job opportunities in data science
Python
- Setup And Environment
- Course preview
- Running python using Javscript
- How to Python Environment in Local System
- How to setup for VS Code
- Level 1 of Python
- Print Statement and Program flow
- Variables
- Datatypes and Typecasting
- Exercise for Variables and Datatypes
- Arithmetic Operations
- Strings- Basics/Slicing
- Exercise for String- Basics and Arithmetic Operation
- Strings Part 2 (Find, replace, string formatting)
- User Input
- List – Basics
- List - Continued
- Exercise for List
- Split and Join
- Split and Join Exercise
- Tuples
- Sets
- Comments
- Level 2 of Python
- Functions (Calling ,Parameter, Arguments, dafaults )
- Exercise for Function
- Function Named Notation
- Return Statement
- Comparisons and Boolean
- Conditionals: If, else, Else if
- Exercise for If, else. Else if
- Exercise Improve in Conditionals
- While Loops
- Exercise for While Loops
- For Loops and nesting
- Exercise for For Loops
- Enumerate This!
- Sort() and Sorted()
- Dictionaries
- Dictionaries 2
- Level 3 of Python
- File Handling – Reading Files
- Exceptions: Try/Except, Raise
- Classes and Objects
- Inheritance
- Modules
- Zip and Unzip
- Lambda Function part 1
- Lambda Function part 2
- Exercise for Lambda Function
- Comprehension List
- Comprehension – Dictionary
- Core Level of Python
- Randomness
- Timeit and performance
- Project – crypto machine
- Project – Math tutor
- Project - Marble/Trading Game
- Guidance for your next python projects
- and so much more...
Machine Learning
Learn DS, ML & Python
without going through any complex words/language
How this course is different than other courses?
Detailed Explanation
Each topic is explained thoroughly, to make your basics strong.
Interview/Exams Oriented
Topics are arranged in alignment with your exams and interview process to eliminate any confusion.
Easy Language
Each topic is an easy read, to make sure you get the core concepts.
Exclusive
This coourse is Academic Fight's Exclusive, meaning you won't find these books at any store or online site.
24/7 Support
If any problem comes even long after purchase, just hit a WhatsApp text and we will be there to answer all your questions.
BONUS
100 Questions Quiz Pack
₹ 999
FREE
ML Interview 100Q Pack
₹ 1,200/-
FREE
₹ 999
Python Resources
FREE
Practical Projects
₹ 5,000
FREE
Support
24/7 WhatsApp support
24/7 Mail support
Do you give GST receipt after purchase?
YES! You get GST receipt in your mail within 5min of purchase
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Basic English
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