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

 Online Class

788   Users Booked

$ 196   $ 84

SYLLABUS

Total Sessions: 41 | Total Hours: 60 Hours

1. 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.
2. Introduction to Machine Learning (1 Session)
All the Basics, Data Science Modules and Application of Machine Learning and its tools
3. 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
4. Introduction to Python Programming and Data Type (1 Session)
Introduction to Python Programming, Python Handson, Data Type, Tuples and Dictionary, List and Set
5. Numpy Packages and its Handson (1 Session)
Numpy Package, Creating arrays, Indexing, Data Processing using Arrays and File Input & Output.
6. Pandas Packages and its Handson (1 Session)
Introduction to Pandas Packages, Pandas Packages Handson, Create the Data frame and List and Data frame and Set and OTS Hanson
7. Data Visualization using Matplotlib Packages (1 Session)
Introduction to Data Visualization, Handson Line Graph, Pie Chart, Bar Graph and Scatter Plot, Handson Exercise using Matplotlib
8. Descriptive Statistics using Pandas (1 Session)
Introduction to Descriptive statistics and central Tendency, Measure of Dispersion, Distribution of Shape and Outliers, Descriptive Statistics Handson
9. Hypothesis Testing and its Process (1 Session)
Introduction to Hypothesis, Hypothesis Formulation and Process, Errors in Hypothesis
SHOW MORE SESSIONS
10. Inferential Statistics using Scipy (1 Session)
Introduction to Inferential statistics, Non Parametric Test using Scipy, Non Parametric Test Handson, Parametric Test handsome using Scipy
11. Data Preparation Process and EDA (1 Session)
This part cover Data Preparation Process and Exploratory Data Analysis (EDA)
12. Measurement and Scaling (1 Session)
This part cover Data Analysis, Comparative Scaling Techniques and Non Comparative Scaling Techniques
13. Data Collection and Data Treatment (1 Session)
This part cover Data Collection Methods and Data Types and Data Source error and Data Treatment
14. Correlation and Application (1 Session)
Introduction to Correlation, Product Moment Correlation and Its Application, Correlation Using Hypothesis Test, Correlation Handson using scipy packages
15. Questionnaire Design Process (1 Session)
This part cover Questionnaire Design Process, Application on Questionnaire Design
16. Linear Regression and its Application (1 Session)
This part cover Linear Regression Introduction, Simple Regression using Hypothesis testing, Multiple Regression and its an Assumption, Simple and Multiple Regression Handson using Python codes
17. Probability and its Bayes Theorem (1 Session)
This part cover Probability Introduction and its Application, Bayes Theorem using Probability
18. Discrete Probability Distribution (1 Session)
Discrete Probability Distribution - Binomial and Poisson, Hyper geometric Distribution and its Application
19. Continuous Probability Distribution (1 Session)
This part cover Continuous Probability, Uniform, Normal and Exponential Distribution, Application of Continuous Probability Distribution
20. ANOVA - Analysis of Variance(1 Session)
This part cover ANOVA Using Hypothesis Testing, Handson using Python
21. ANCOVA - Analysis of Co-variance (1 Session)
This part cover ANCOVA using Hypothesis Testing and Handson
22. Discriminant Analysis(1 Session)
This part cover Discriminant Analysis and its Application and Handson using Python
23. Logistic Regression (1 Session)
Logistic Regressions Methods and Application and Handson using Sklearn Packages
24. Time Series (1 Session)
This part cover Components and Smoothing and Forecasting Methods and Handson
25. Factor Analysis (1 Session)
This part cover PCA and Rotation Method, Real Time Case Study and Hands on Using Sklearn
26. Cluster Analysis (1 Session)
This part Hierarchical Clustering, Non Hierarchical Clustering and Handson Using Cluster Analysis
27. Data Architecture (1 Session)
This part cover Data Architecture and Data Warehousing and Multidimensional Model
28. Association Rule - Apriori Algorithm (1 Session)
This part Apriori Algorithm, Market Basket Analysis and Handson using Apriori Algorithm
29. Artificial Neural Network (1 Session)
This part cover Single and Multi Layered Architecture, ANN With Real time Example
30. Decision Tree (1 Session)
Introduction and Implementation Logics and Handson using Sklearn Packages
31. Random Forest (1 Session)
Introduction and Implementation Logics and Handson using Sklearn Packages
32. Naive Bayes Classification (1 Session)
Naive Bayes - Introduction and Implementation Logics and Naive Bayes - Handson using Sklearn Packages
33. K Nearest Neighbor (1 Session)
KNN - Introduction and Implementation Logics and KNN - Handson using Sklearn Packages
34. Support Vector Machine (1 Session)
SVM - Introduction and Implementation Logics and SVM - Handson using Sklearn Packages
35. Experimental Deign (1 Session)
Basic Experimental Design and its Application and Complex Experimental Design and its Application
36. Image Processing (1 Session)
Image Processing and its application and Image Processing and its histogram features
37. Image Recognition (1 Session)
Image Recognition and its Application and Viola Jones Algorithm and its Application
38. 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.
39. 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
40. Machine Learning complete Recapsulation(1 Session)
Machine Learning - Statistics and Visualization and Supervised and Unsupervised Algorithm
41. Build your Github profile (1 Session)
Learn how to create your Github profile and how to add the code snippet to it. Bonus: Learn why Github is important for your CV and how to add Github profile to your CV.
CLASS SUMMARY
Is this yet another online course? NO. This is Braingroom's signature 'Nudge factor' program.
Wait, what is a Nudge factor program? Your course completion is our end goal. Our commitment to you doesn't just end when you buy the course. We are committed to get you to COMPLETE this course within a time frame (thats mutually agreed upon while you sign up with us). We also carve out a personalized study plan for you based on many parameters, to ensure that your time is well utilised. We very well understand that you, as a working professional, will already be working for 9-10 hours a day. So, we guarantee that the 1-2 hours (daily) that you decide to invest with us, will be put to your good use.
Highlights of Braingroom's Nudge factor program:
1. You will have a dedicated and personal mentor who will be in touch with you daily (through whats app) and help you stay motivated to complete your daily lesson targets. Why whats app? Well, we promised to nudge you to complete the course and we find whats app perfect for this.
2. You will receive a FREE personalized study plan (based on your availability, target and comfort level). You and your mentor will be following this schedule very religiously.
3. Our mentor's goal will be to help you complete this 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 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.
Reviews (9)
Kamal
Kamal
25 September 2019
This way of communicating with the whats app allows me to ask my doubts and questions right away.
Sulochana
Sulochana
22 September 2019
I learned this class in less time, spending only two hours every day with the Nudge method
DHANANJAY KUMAR
DHANANJAY KUMAR
17 September 2019
I thought that I should study python to study machine learning but I learned to combine both in this class.
Neela
Neela
04 September 2019
I had minimum program knowledge but after completing this online class I have a good knowledge of programming. Thanks Braingroom
Gurunath
Gurunath
30 August 2019
This nudge method was very helpful to me so that I could finish this ml course within a certain time.
chetan verma
chetan verma
26 August 2019
Great course
Ram
Ram
04 August 2019
I am very much confident in machine learning after learning the machine learning online course through braingroom.
Krishnan
Krishnan
24 July 2019
excellent tutor and his teaching style is good so far on the entire braingroom website.
Tushar Mehndiratta
Tushar Mehndiratta
06 July 2019
The program was very well structured and concepts were taught in a very practical way.....Looking for more such camps in future
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