Data Analytics, Machine Learning and Artificial Intelligence - Online course using Python

 Online Class

540   Users Booked

S$ 252   S$ 105

SYLLABUS

Total Sessions: 39 | Total Hours: 60 Hours

1. Introduction to Machine Learning(1 Session)
All the Basics, Data Science Modules and Application of Machine Learning and its tools
  Self Assessment Test
2. 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
  Self Assessment Test
3. 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
  Self Assessment Test
4. Numpy Packages and its Handson(1 Session)
Numpy Package, Creating ndarrays, Indexing, Data Processing using Arrays and File Input & Output.
  Self Assessment Test
5. 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
  Self Assessment Test
6. 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
  Self Assessment Test
7. Descriptive Statistics using Pandas(1 Session)
Introduction to Descriptive Statistics and Central Tendency, Measure of Dispersion, Distribution of Shape and Outliers, Descriptive Statistics Handson
  Self Assessment Test
8. Hypothesis Testing and its Process(1 Session)
Introduction to Hypothesis, Hypothesis Formulation and it's process and Errors in Hypothesis
  Self Assessment Test
9. Inferential Statistics using Scipy(1 Session)
Introduction to Inferential Statistics, Non Parametric Test using Scipy, Non Parametric Test Handson, Parametric Test Handson using Scipy
  Self Assessment Test
SHOW MORE SESSIONS
10. Data Preparation Process and EDA(1 Session)
Data Preparation Process and Exploratory Data Analysis (EDA)
  Self Assessment Test
11. Measurement and Scaling(1 Session)
Primary Scales - Data Analysis, Comparitive Scaling Techniques, and Non Comparitive Scaling Techniques
  Self Assessment Test
12. Data Collection and Data Treatment(1 Session)
Data Collection Methods & Data Types and Data Source error & Data Treatment
  Self Assessment Test
13. Correlation and Application(1 Session)
Introduction to Correlation, Product Movement Correlation and its application, Correlation using Hypothesis Test, Correlation Handson using Scipy Packages
  Self Assessment Test
14. Questionnaire Design Process(1 Session)
Questionnaire Design Process and Application on Questionnaire Design
  Self Assessment Test
15. 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
  Self Assessment Test
16. Probability and its Baye's Theorem(1 Session)
Probability Introduction and its Application and Bayes Theorem using Probability
  Self Assessment Test
17. Discrete Probability Distribution(1 Session)
Discrete Probability Distribution - Binomial and Possion and Hypergeometric Distribution and it's Application
  Self Assessment Test
18. Continuous Probability Distribution(1 Session)
Continuous Probability - Uniform, Normal and Exponential Distribution and Application of Continuous Probability Distribution
  Self Assessment Test
19. ANOVA - Analysis of Variance(1 Session)
ANOVA Using Hypothesis Testing and ANOVA - Handson using Python
  Self Assessment Test
20. ANCOVA - Analysis of Covariance(1 Session)
ANCOVA using Hypothesis Testing and Handson
  Self Assessment Test
21. Discrimiant Analysis(1 Session)
Discriminant Analysis and it's Application and Discriminant Analysis - Handson using Python
  Self Assessment Test
22. Logistic Regression(1 Session)
Logistic Regression - Methods and Application and Logistic Regression - Handson using Sklearn Packages
  Self Assessment Test
23. Time Series(1 Session)
Timeseries - Components and Smothening and Timeseries - Forecasting Methods and Handson
  Self Assessment Test
24. Factor Analysis(1 Session)
Factor Analysis - PCA and Rotation Method, Factor Analysis - Real Time Case Study and Factor Analysis - Hands on Using Sklearn
  Self Assessment Test
25. Cluster Analysis(1 Session)
Hierarchical Clustering, Non Hierarchical Clustering and Handson Using Cluster Analysis
  Self Assessment Test
26. Data Architecture(1 Session)
Data Architecture and Data Warehousing and Multi Dimentional Model
  Self Assessment Test
27. Association Rule - Apriori Algorithm(1 Session)
Apriori Algorithm, Market Basket Analysis and Handson using Apriori Algorithm
  Self Assessment Test
28. Artificial Neural Network(1 Session)
ANN - Single and Multi Layered Architecture and ANN - With Real time Example
  Self Assessment Test
29. Decision Tree(1 Session)
Decision Tree - Introduction and Implementation Logics and Decision Tree - Handson using Sklearn Packages
  Self Assessment Test
30. Random Forest(1 Session)
Random Forest - Introduction and Implementation Logics and Random Forest - Handson using Sklearn Packages
  Self Assessment Test
31. Naive Bayes Classification(1 Session)
Naive Bayes - Introduction and Implementation Logics and Naive Bayes - Handson using Sklearn Packages
  Self Assessment Test
32. K Neariest Neighbour(1 Session)
KNN - Introduction and Implementation Logics and KNN - Handson using Sklearn Packages
  Self Assessment Test
33. Support Vector Machine(1 Session)
SVM - Introduction and Implementation Logics and SVM - Handson using Sklearn Packages
  Self Assessment Test
34. Experimental Deign(1 Session)
Basic Experimental Design and it's Application and Complex Experimental Design and it's Application
35. Image Processing(1 Session)
Image Processing and it's application and Image Processing and it's histogram features
36. Image Regogonition(1 Session)
Image Recognition and it's Application and Viola Jones Algorithm and it's Application
37. 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.
38. 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, Company’s Financial Analytics
39. Machine Learning complete Recapsulation(1 Session)
Machine Learning - Statistics and Visualisation and Supervised and Unsupervised Algorithm
CLASS SUMMARY
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 Company's 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 learner's convenience & availability
ABOUT CLASS
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
The 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 India's 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.
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