
Explore cognitive computing, its definitions, and how it aims to simulate human thought through unstructured data, natural language processing, and self-learning within automated systems.
Discover how statistics underpin business decisions through descriptive statistics and inference. Explore data collection, sampling, and testing as tools to understand populations and inform finance and government applications.
Explore the standard normal distribution through hands-on examples, calculating probabilities and p-values, and formulating and testing a null hypothesis about a population mean.
Learn to perform a one-way ANOVA to compare group means, test the null hypothesis that all means are equal, and interpret p-values for treatment effects.
Explore grouped data with frequency distributions, histograms, and cumulative frequency, and learn to interpret percentiles and quartiles using Pareto and multi-variance charts for data-driven decisions.
Learn Python date and time handling, parsing and formatting dates across different frames, and extracting day, month, and weekday using flexible formatting tokens.
Explore saving any object you create and managing its structure. Then apply common mathematical functions like absolute value, natural logarithm, floor, and concatenation in data workflows.
Explore standard normal concepts and the normal density function, compute cumulative normal values, and visualize with plots and histograms while simulating normal, uniform, and Poisson distributions.
The AI world is too big to comprehend. The AI has been most talked about for last few years and the knowledge has been spread across multiple places. As practitioner of AI, I am trying to bring many relevant topics under one umbrella in following topics.
1. Various terms used under the umbrella of AI
2. Understand and use Basic Statistics (90% hands on and 10% theory)
3. Basic Python (90% hands on and 10% theory)
4. Basic R (90% hands on and 10% theory)
5. Will able to understand the various terms used under the umbrella of Machine learning (ML)