Machine Learning Combo Course using Python and Mongodb Best of Both worlds to become an ML expert!

SYLLABUS

Total Sessions: 79 | Total Hours: 80 Hours

1. Introduction(1 Session)
Course structure, brief intro about Python, Advantages, Applications and Opportunities

2. Installation and Execution(1 Session)
Setup and Execution, Interactive Prompts, OS terminals, IDEs, IDLE and explanation of Jupyter Notebooks

3. Variables and Datatypes(1 Session)
Naming Variables, Functions, Classes etc.,

4. Numbers and Operator(1 Session)
Integers, Floating point numbers, Complex, Binary, Octal, Hexadecimal, Long

5. Strings and String functions - Part 1(1 Session)
Represent Characters, Words or Sentences, Single, Double or Triple Quotes

6. Strings and String functions - Part 2(1 Session)
Indexing

7. Strings and String functions - Part 3(1 Session)
String Functions

8. Lists(1 Session)
List Indexing, List Slicing, Task, List Append, List Clear, List Extend

9. Dictionaries(1 Session)
Key-Value Pairs, Syntax, Unordered

SHOW MORE SESSIONS

10. Sets and Tuples(1 Session)
Unordered Collection, Can contain numbers, strings or tuples, Represented using, Repetitions not allowed, Operations resemble set theory in mathematics

11. Statements & Conditionals(1 Session)
Assignments, Function statements, Control statements, Namespaces, Generators, Exceptions, Debuggers, Context managers, Import Statements, Object declarations

12. Loops(1 Session)
Conditional Statement & Loop Statement

13. Statements & Conditionals Part 2(1 Session)
Continuation of Statements & Conditionals Part 2

14. Functions - Part 1(1 Session)
Functions Intro & Arguments

15. Functions - Part 2(1 Session)
Variable Scopes

16. Exception Handling and Modules(1 Session)
Exception Handling and Modules

17. Classes(1 Session)
Introduction to OOP, Classes, Objects

18. Application 1(1 Session)
Project Intro, Text Files, Quiz & Quiz Solutions and JSON and XML Files

19. Application 2(1 Session)
Reading CSV Data and Writing to Excel File

20. Application 3(1 Session)
HTTP & API Requests

21. MongoDB Introduction and Installation(1 Session)
MongoDB Installation in Windows and Linux

22. MongoDB Crud Operations(1 Session)
Crud Operations

23. Data Modeling(1 Session)
Data Modeling and Index

24. Administration(1 Session)
Administration

25. Tools(1 Session)
MongoDB Tools

26. Security(1 Session)
MongoDB Security

27. Aggregation(1 Session)
MongoDB Aggregation

28. Replication(1 Session)
MongoDB Replication

29. Sharding(1 Session)
MongoDB Sharding

30. OPS Manager(1 Session)
Ops Manager

31. Introduction to Machine Learning (1 Session)
All the Basics, Data Science Modules and Application of Machine Learning and its tools

32. Introduction to Python and IDE(1 Session)
Different flavors and platforms, Install Anaconda, Python IDE: Spyder, Setting your work directory, The libraries that you should know, IPython and Python Notebook

33. Introduction to Python Programming and Data types(1 Session)
Concept of Program and Programming, Programming constructs in Python. Values, Variables, Strings, Tuples, Lists & Dictonaries, Accessing Elements. Cloning Slices, Aliasing Vs Cloning and Iterations

34. Numpy Packages and its Handson(1 Session)
Numpy Package, Creating ndarrays, Indexing, Data Processing using Arrays and File Input & Output.

35. Pandas Packages and its Handson(1 Session)
Introduction to Pandas Packages, Pandas Packages Handson, Create the Dataframe and List and Dataframe and Set and OTS Handson

36. Data Visualization using Matplotlib Packages(1 Session)
Introduction to Data Visualization, Handson Line Graph, Pie Chart, Bar Graph and Scatter Plot, Handson Exercise using Matlotlib

37. Descriptive Statistics using Pandas (1 Session)
Introduction to Descriptive Statistics and Central Tendency, Measure of Dispersion, Distribution of Shape and Outliers, Descriptive Statistics Handson

38. Hypothesis Testing and its Process (1 Session)
Introduction to Hypothesis, Hypothesis Formulation and its process and Errors in Hypothesis

39. Inferential Statistics using Scipy(1 Session)
Introduction to Inferential Statistics, Non Parametric Test using Scipy, Non Parametric Test Handson, Parametric Test Handson using Scipy

40. Data Preparation Process and EDA(1 Session)
Data Preparation Process and Exploratory Data Analysis (EDA)

41. Measurement and Scaling(1 Session)
Primary Scales - Data Analysis, Comparitive Scaling Techniques, and Non Comparitive Scaling Techniques

42. Data Collection and Data Treatment(1 Session)
Data Collection Methods & Data Types and Data Source error & Data Treatment

43. Correlation and Application(1 Session)
Introduction to Correlation, Product Movement Correlation and its application, Correlation using Hypothesis Test, Correlation Handson using Scipy Packages

44. Questionnaire Design Process (1 Session)
Questionnaire Design Process and Application on Questionnaire Design

45. Linear Regression and its Application (1 Session)
Linear Regression Introduction, Simple Regression using Hypothesis Testing, Multiple Regression and its assumption, and Simple and Multiple Regression Handson using Python codes

46. Probability and its Bayes Theorem (1 Session)
Probability Introduction and its Application and Bayes Theorem using Probability

47. Discrete Probability Distribution(1 Session)
Discrete Probability Distribution - Binomial and Possion and Hypergeometric Distribution and its Application

48. Continuous Probability Distribution (1 Session)
Continuous Probability - Uniform, Normal and Exponential Distribution and Application of Continuous Probability Distribution

49. ANOVA - Analysis of Variance(1 Session)
ANOVA Using Hypothesis Testing and ANOVA - Handson using Python

50. ANCOVA - Analysis of Covariance(1 Session)
ANCOVA using Hypothesis Testing and Handson

51. Discrimiant Analysis(1 Session)
Discriminant Analysis and its Application and Discriminant Analysis - Handson using Python

52. Logistic Regression(1 Session)
Logistic Regression - Methods and Application and Logistic Regression - Handson using Sklearn Packages

53. Time Series(1 Session)
Timeseries - Components and Smothening and Timeseries - Forecasting Methods and Handson

54. Factor Analysis(1 Session)
Factor Analysis - PCA and Rotation Method, Factor Analysis - Real Time Case Study and Factor Analysis - Hands on Using Sklearn

55. Cluster Analysis(1 Session)
Hierarchical Clustering, Non Hierarchical Clustering and Handson Using Cluster Analysis

56. Data Architecture(1 Session)
Data Architecture and Data Warehousing and Multi Dimentional Model

57. Association Rule - Apriori Algorithm(1 Session)
Apriori Algorithm, Market Basket Analysis and Handson using Apriori Algorithm

58. Artificial Neural Network(1 Session)
ANN - Single and Multi Layered Architecture and ANN - With Real time Example

59. Decision Tree(1 Session)
Decision Tree - Introduction and Implementation Logics and Decision Tree - Handson using Sklearn Packages

60. Random Forest (1 Session)
Random Forest - Introduction and Implementation Logics and Random Forest - Handson using Sklearn Packages

61. Naive Bayes Classification(1 Session)
Naive Bayes - Introduction and Implementation Logics and Naive Bayes - Handson using Sklearn Packages

62. K Neariest Neighbour (1 Session)
KNN - Introduction and Implementation Logics and KNN - Handson using Sklearn Packages

63. Support Vector Machine (1 Session)
SVM - Introduction and Implementation Logics and SVM - Handson using Sklearn Packages

64. Artificial Neural Network (ANN)(1 Session)
Artificial Neural Network Introduction, ANN - Architecture and Schematic Diagram, ANN – Architectural Types, Pre-processing steps of ANN etc.,

65. K - Nearest Neighbour (KNN) (1 Session)
K - Nearest Neighbour Introduction, K - Nearest Neighbour Algorithm, Pre-Processing your dataset for KNN, How to measure "Neraby" etc.,

66. Time Series (1 Session)
Time Series Basics, Time Series Component, Smoothing Methods, Trend Based Forecasting, Time Series using R Programming, Auto Regressive (AR) Model etc.,

67. Marketing Analytics and its Case Study(1 Session)
Introduction to Retail and Marketing Analytics, Customer Analytics, Churn Modelling using Operational Analytics, Market Basket Analysis using Marketing Analytics, Price and In store Promotion using Retail Analytics.

68. Operational Analytics and its Case Study (1 Session)
Scope of the Project and Economic Industry Analysis, Company Analysis and its Competitor Analysis, Project Specific Analysis and Theoretical Framework, Limitations, Findings and Recommendation.

69. Finance Analytics and its Application (1 Session)
Credit Risk Analytics using Logistic Regression, Merger and Acquisitions Analysis using Financial Analytics, Financial Ratio Analysis using Analytics, Companys Financial Analytics

70. Project Session - How to use the inbuilt AI based Code Editor(1 Session)
Learn why we have this super cool AI based code editor for you. Also, learn how to make the best use of it.

71. Project Session - Web scraping and Automation using Python - Part 1(1 Session)
This part covers BeautifulSoup module HTML Parsing of web pages,Crawling links Storing Data in files.

72. Project Session - Web scraping and Automation using Python - Part 2(1 Session)
This part covers Automatic Scraping using Selenium Handling, API requests Bottle necks in Scraping.

73. Project Session - Data mining and Visualization from Google sheets - Part 1(1 Session)
This part covers Numerical computation in python using Numpy, Pandas, Scipy. Visualisation using Matplotlib, Accessing Google sheets data using Google sheets API

74. Project Session - Data mining and Visualization from Google sheets - Part 2(1 Session)
This part covers Project Intro, Text Files, Quiz & Quiz Solutions and JSON and XML Files, Reading CSV Data and Writing to Excel File, HTTP & API Requests

75. Project Session - API Integration Using Python(1 Session)
This part covers advanced topics in Python. You will be integrating two third party APIs and create your own project that you can show off on Github.

76. Project Session - Realtime project suggestions from industry experts(1 Session)
Get introduced to few Project ideas that you can work on by yourself.

CLASS SUMMARY

Python Course:
Python course is completely crafted and handled by an industry expert cum Trainer who has own company in Learning and Development Industry. Mr Kamalesh kishore, CEO of Kalviakam Education Services LLP, Chennai and Tollun Technologies LLP, Chennai has vast experience in conducting Python training programs in many colleges in Chennai. He is also working as a Freelance developer for many MNCs.
MongoDB Course:
Course is designed and handled by Mr.Jagadeesan, Senior Associate in an MNC. Through his career spanning more than 8 years, he has worked with several corporates and has helped them scale and outgrow critical problem by building robust systems
Machine Learning and Artificial Intelligence through Python Course:
The course is taken by Dinesh Babu a CBAP certified professional and a Senior Business Analyst. He undertakes Data Analysis Training for Indian as well as students overseas.During his teaching with spans over 8 years, his deep knowledge and expertise on the subject has won his several awards the most notable of which is the Best Data Science Teacher in Delhi- 2017.
This course gives an introduction into the field of business intelligence and business analytics-domains which extensively use data to take decisions. The course delves into statistics, quantitative analysis, exploratory predictive models, and fact-based management which are indispensable tools for any aspiring data scientist/analyst.
Following are the specialties of the program:
1. We have a live prime time questions support in the app itself with the tutor (https://play.google.com/store/apps/details?id=com.braingroom.user)
2. 20minute rule Quiz questions on topics learned in the interval of every twenty minutes
3. Weekly assignments and prereading / postreading content will be shared as project material
4. Curated course conducted with inputs from industry experts
5. Internship opportunities for students qualifying the final evaluation, We are partnering with several tech startups across India to give students real time work experience
6. Internship with various startups in India for individuals who clears Companys assessment
7. Tutor time Live Q & A Session with Tutor once a week
8. Online classes can be taken at any place and at any time as per the learners convenience & availability

ABOUT CLASS

About Python Course:
Course covers everything starting from basics to advanced of Python & MongoDB Concepts. Even Students without basic Python & MongoDB understanding can easily grasp concepts as everything is being taught from basics in a detailed manner.
The Python course will cover the following topic such as Execution, Variables and Numbers, Strings, Conditionals, Functions, OOPS etc.
About MongoDB Course:
The MongoDB course will cover the following topic such as RDBMS Vs NoSQL, MongoDB Installation in Windows and Linux, Storage, Aggregation, Backup and Recovery, MongoDB Cloud & Ops Manager Fundamentals etc.,
About Machine Learning Course:
Course gives you a comprehensive understanding of Machine Learning right from basic to advanced levels. Even Students without basic understanding of Machine Learning can take up this course.
The Machine Learning course will cover the following topic such Introduction to Machine Learning, Numpy Packages and its Handson, Pandas Packages and its Handson, Data Preparation Process and EDA, Linear Regression and its Application, ANOVA - Analysis of Variance, ANCOVA - Analysis of Covariance, Time Series, Marketing Analytics and its Case Study, Finance Analytics and its Application etc.,

ABOUT ACADEMY

About Python Tutor:-
Course is designed and taught by Mr Kamalesh Kishore, an industry expert cum Trainer who is the CEO of two companies - Kalviakam Education Services LLP and Tollin Technologies LLP, Chennai. His vast knowledge has attracted the interest of several prestigious colleges in Chennai and they have partnered up with him for training their students. He also does consult for companies.
About Mongo DB Tutor:-
Course is designed and handled by Mr.Jagadeesan, Senior Associate in an MNC. Through his career spanning more than 8 years, he has worked with several corporates and has helped them scale and outgrow critical problem by building robust systems. His ability to express complex ideas in a simple and digestible manner makes him an indispensable teacher, his popularity with students stands as a testament to this. He has experience of conduction Workshops on MongoDB in various corporate companies and he regularly takes online classes to students across the Globe.
So take the plunge and let an expert hand guide you through the exciting world of Mongo DB
About Machine Learning Tutor:
Dinesh Babu ,B.Tech,MBA,Ph.D with dual specialization in Finance and Operation. He completed Ph. D. in Data Analysis from BITS, Pilani. Currently,a Senior Business Analyst in a MNC.His zeal and passion for up-scaling youth has propelled him into teaching and the 8 years that followed has seen him get several awards and citations.
Braingroom Skill Centre:-
We are happy to host Mr. Dinesh Babu energetic tutor in our portal for conducting Machine Learning through Python. Currently under the elite FACEBOOK SheLeadsTech India mentorship program, Braingroom has built a name for itself being an education and learning platform where we have a wide offering of multilingual online and offline classes with the vision of upskilling Indias youth. With 20000+ Online Leaners, 2000+ Classes in 55+ Categories and with an experience of having trained 30000+ students (Online + Offline) and expertise/Industry connects enabling 100s of Students Internships, Braingroom Skill centre stands tall among competition in the online learning space as the only complete online learning ecosystem in the country.

Reviews (15)