Python Introduction to Python and IDEs - The fundamentals of the Python programming language, including how to utilize Jupyter, Pycharm, and other IDEs for Python development. The fundamentals of Python: variables, data types, loops, conditional statements, functions, decorators, lambda functions, handling of files and exceptions, etc. The basics of object-oriented programming, including classes, objects, inheritance, abstraction, polymorphism, and encapsulation. Practice assignments and hands-on sessions - the convergence of all the aforementioned ideas with examples of real-world problems to aid in understanding. Linux Introduction to Linux - Establishing the essential understanding of how Linux OS functions and how to start using it. Linux Fundamentals: Handling files, extracting data, etc. Practice assignments and hands-on sessions - carefully chosen problems for you to begin with on Linux.

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Excel Fundamentals Reading the Data, Referencing in formulas , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security VBA Macros, Ranges and Worksheet in VBA IF conditions, loops, Debugging, etc. Excel For Data Analytics Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc. Data Visualization with Excel Charts, Pie charts, Scatter and bubble charts Bar charts, Column charts, Line charts, Maps Multiples: A set of charts with the same axes, Matrices, Cards, Tiles Excel Power Tools Power Pivot, Power Query and Power View Classification Problems using Excel Binary Classification Problems, Confusion Matrix, AUC and ROC curve Multiple Classification Problems Information Measure in Excel Probability, Entropy, Dependence Mutual Information Regression Problems Using Excel Standardization, Normalization, Probability Distributions Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression Hands-on Exercise: Classification problem using excel on sales data, and statistical tests on various samples from the population.

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SQL Basics – Fundamentals of Structured Query Language SQL Tables, Joins, Variables Advanced SQL – SQL Functions, Subqueries, Rules, Views Nested Queries, string functions, pattern matching Mathematical functions, Date-time functions, etc. Deep Dive into User Defined Functions Types of UDFs, Inline table value, multi-statement table. Stored procedures, rank function, SQL ROLLUP, etc. SQL Optimization and Performance Record grouping, searching, sorting, etc. Clustered indexes, common table expressions. Hands-on exercise: Writing comparison data between past year to present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data, and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

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Extract Transform Load Web Scraping, Interacting with APIs Data Handling with NumPy NumPy Arrays, CRUD Operations, etc. Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices. Data Manipulation Using Pandas Loading the data, dataframes, series, CRUD operations, splitting the data, etc. Data Preprocessing Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc. Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross validation techniques, train-test split, etc. Data Visualization Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc. with Python matplotlib. Regression plots, categorical plots, area plots, etc, with Python seaborn.

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Descriptive Statistics – Measure of central tendency, measure of spread, five points summary, etc. Probability Probability Distributions, bayes theorem, central limit theorem. Inferential Statistics – Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

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Introduction to Machine learning Supervised, Unsupervised learning. Introduction to scikit-learn, Keras, etc. Regression Introduction classification problems, Identification of a regression problem, dependent and independent variables. How to train the model in a regression problem. How to evaluate the model for a regression problem. How to optimize the efficiency of the regression model. Classification Introduction to classification problems, Identification of a classification problem, dependent and independent variables. How to train the model in a classification problem. How to evaluate the model for a classification problem. How to optimize the efficiency of the classification model. Clustering Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables. How to train the model in a clustering problem. How to evaluate the model for a clustering problem. How to optimize the efficiency of the clustering model. Supervised Learning Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc. Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc. Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions. Random Forest – Creating random forest models for classification problems in a supervised learning approach. Support Vector Machine – SVM or support vector machines for regression and classification problems. Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding local minimum and maximum of a given function. K-Nearest Neighbors – A simple algorithm that can be used for classification problems. Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting. Unsupervised Learning K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach. Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation. Linear Discriminant Analysis – LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data. Principal Component Analysis – PCA follows the same approach in handling the multidimensional data. Performance Metrics Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc. Confusion matrix – To evaluate the true positive/negative, false positive/negative outcomes in the model. r2, adjusted r2, mean squared error, etc.

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Introduction to MLOps MLOps lifecycle MLOps pipeline MLOps Components, Processes, etc Deploying Machine Learning Models Introduction to Azure Machine Learning Deploying Machine Learning Models using Azure

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Artificial Intelligence Basics Introduction to keras API and tensorflow Neural Networks Neural networks Multi-layered Neural Networks Artificial Neural Networks Deep Learning Deep neural networks Convolutional Neural Networks Recurrent Neural Networks GPU in deep learning Autoencoders, restricted boltzmann machine

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Power BI Basics Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI. SaaS Connectors, Working with Azure SQL database, Python and R with Power BI Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI. DAX Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features Data Visualization with Analytics Slicers, filters, Drill Down Reports Power BI Query, Q & A and Data Insights Power BI Settings, Administration and Direct Connectivity Embedded Power BI API and Power BI Mobile Power BI Advance and Power BI Premium Hands-on Exercise: Creating a dashboard to depict actionable insights in sales data.

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Recommendation Engine - The case study will walk you through several machine learning procedures and methods to create a recommendation engine that can be used to suggest movies, restaurants, books, and other media. Rating Predictions - This case study on text categorization and sentiment analysis will show you how to use text data to develop effective machine learning models that can forecast ratings, sentiments, and other things. Census: By using predictive modelling approaches to census data, you may develop actionable insights for a specific community and build machine learning models that can forecast or categorise different parameters like the total population, user income, and so forth. Housing - This real estate case study will point you toward issues that arise in the actual world as a result of the combination of several factors. A considerably more complex yet straightforward case study on object detection will help you create a machine learning model that can identify items in real time. Stock Market Analysis - Using historical stock market data, you will discover how feature engineering and feature selection may provide you some incredibly useful and practical knowledge for particular stocks. The banking problem is a categorization issue where machine learning models are used to forecast customer behaviour based on a variety of parameters. AI Chatbot - You may use machine learning techniques to build an AI chatbot using the NLTK Python package.

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