Sanjeev Kumar Prajapati
Dehradun/Online
Skills
About
My name is Sanjeev Kumar Prajapati, and I am from Rudrapur, Uttarakhand. I hold a degree in Computer Science with a specialization in Artificial Intelligence and Machine Learning f...
My name is Sanjeev Kumar Prajapati, and I am from Rudrapur, Uttarakhand. I hold a degree in Computer Science with a specialization in Artificial Intelligence and Machine Learning from Quantum University.With four years of experience in technical domains, I currently work as a Software Engineer at Jio. My expertise spans Data Science, AI with Python, Machine Learning, Deep Learning frameworks (TensorFlow, PyTorch, Keras), MLOps, Power BI, R Programming, and C Programming. I focus on practical, hands-on solutions and continuously strive to apply advanced technologies to solve real-world problems.
Sanjeev Kumar Prajapati
Dehradun/Online
Skills :
My name is Sanjeev Kumar Prajapati, and I am from Rudrapur, Uttarakhand. I hold a degree in Computer Science with a specialization in Artificial Intelligence and Machine Learning f...
My name is Sanjeev Kumar Prajapati, and I am from Rudrapur, Uttarakhand. I hold a degree in Computer Science with a specialization in Artificial Intelligence and Machine Learning from Quantum University.With four years of experience in technical domains, I currently work as a Software Engineer at Jio. My expertise spans Data Science, AI with Python, Machine Learning, Deep Learning frameworks (TensorFlow, PyTorch, Keras), MLOps, Power BI, R Programming, and C Programming. I focus on practical, hands-on solutions and continuously strive to apply advanced technologies to solve real-world problems.
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Courses by: Sanjeev Kumar Prajapati
Python Programming
I am Sanjeev Kumar Prajapati, a dedicated trainer with 4 years of experience in Python Programming, Full Python, and R & Python. I focus on delivering practical, hands-on learning that equips students with the skills needed for data science, analytics, and programming projects.
Through structured lessons, real-world projects, and interactive exercises, I help learners gain a strong understanding of programming concepts, data manipulation, and analysis, preparing them to excel in their careers and become industry-ready.
Module 1: Python Programming Basics
Introduction to Python
Variables, Data Types, Operators
Control Structures: Loops & Conditionals
Functions & Modules
Module 2: Advanced Python (Full Python)
Object-Oriented Programming in Python
File Handling & Exceptions
Python Libraries: NumPy, Pandas, Matplotlib
Data Manipulation and Visualization
Module 3: R Programming Basics
Introduction to R
Data Types & Variables in R
Functions, Loops, and Conditional Statements
Data Import & Export
Module 4: R & Python Integration
Using R and Python together for data analysis
Statistical computing in R
Data visualization using Python & R
Module 5: Data Science & Analytics Projects
Hands-on projects with Python and R
Exploratory Data Analysis (EDA)
Real-world datasets and problem-solving
Module 6: Capstone Project
End-to-end project using Python and R
Reporting, visualization, and insights generation
Industry-relevant use cases
Machine Learning/Data Science
Data Science Syllabus
1.) Python for Data Science
2.) Introduction to Statistics
Ø Types of Statistics
Ø Analytics Methodology and ProblemSolving Framework
Ø Populations and samples
Ø Parameter and Statistics
Ø Uses of variable: Dependent and Independent variable
Ø Types of Variable: Continuous and categorical variable
3.) Descriptive Statistics
4.) Probability Theory and Distributions
5.) Picturing your Data
Ø Histogram
Ø Normal Distribution
Ø Skewness, Kurtosis
Ø Outlier detection
6.) Inferential Statistics
7.) Hypothesis Testing
8.) Analysis of variance (ANOVA)
Ø Two sample t-Test
Ø F-test
Ø One-way ANOVA
Ø ANOVA hypothesis
Ø ANOVA Model
Ø Two-way ANOVA
9.) Regression
Ø Exploratory data analysis
Ø Hypothesis testing for correlation
Ø Outliers, Types of Relationship,scatter plot
Ø Missing Value Imputation
Ø Simple Linear Regression Model
Ø Multiple Regression
Ø Model Building and Evaluation
10.) Model post fitting for Inference
Ø Examining Residuals
Ø Regression Assumptions
Ø Identifying Influential Observations
Ø Detecting Collinearity
11.) Categorical Data Analysis
Ø Describing categorical Data
Ø One-way frequency tables
Ø Association
Ø Cross Tabulation Tables
Ø Test of Association
Ø Logistic Regression
Ø Model Building
Ø Multiple Logistic Regression and Interpretation
12.) Model Building and scoring for Prediction
Ø Introduction to predictive modeling
Ø Building predictive model
Ø Scoring Predictive Model
Ø Introduction to Machine Learning and Analytics
13.) Introduction to Machine Learning
Ø What is Machine Learning?
Ø Fundamental of Machine Learning
Ø Key Concepts and an example of ML
Ø Supervised Learning
Ø Unsupervised Learning
14.) Linear Regression with one variable
Ø Model Representation
Ø Cost Function
Ø Parameter Learning
Ø Gradient Descent
15.) Linear Regression with Multiple Variable
Ø Computing parameter analytically
Ø Ridge, Lasso, Polynomial Regression
16.) Logistic Regression
Ø Classification
Ø Hypothesis Testing
Ø Decision Boundary
Ø Cost Function and Optimization
17.) Multiclass Classification
18.) Regularization
Ø Overfitting, Under fitting
19.) Model Evaluation and Selection
Ø Confusion Matrix
Ø Precision-recall and ROC curve
Ø Regression Evaluation
20.) Support Vector Machine
21.) Decision Tree, Random Forest
22.) Unsupervised Learning
Ø Clustering
Ø K-mean Algorithm
23.) Dimensionality Reduction
Ø Principal Component Analysis and applications
24.) Introduction to Neural Network
Data Analytics
Data Analytics Syllabus
1.) Python Fundamentals for Data Analysis
o Python Data Structure
o Control Statement
o Functions
o Object Oriented Programming concept using Classes
o Objects and Methods
o Exceptions Handling
o Implementation of User Define modules and Packages
o File Handling using python
2.) Introduction to Data understanding and preprocessing
o Knowledge domains of Data Analysis
o Understanding structured and unstructured data
o Data Analysis process
o Dataset generation
o Importing Dataset: Importing and Exporting Data
o Basic Insights from Datasets
o Cleaning and Preparing the Data: Identify and Handle Missing Values
3.) Data Processing and Visualization
o Data Formatting
o Exploratory Data Analysis
o Filtering and hierarchical indexing using Pandas
o Data Visualization:-
§ Basic Visualization Tools
§ Specialized Visualization Tools
§ Seaborn Creating and Plotting Maps
4.) Mathematical and Scientific applications for Data Analysis
o Numpy and Scipy Package
o Understanding and creating N-dimensional arrays
o Basic indexing and slicing
o Boolean indexing
o Fancy indexing
o Universal functions
o Data processing using arrays
o File input and output with arrays
5.) Analysing Web Data
o Combing and merging data sets
o Reshaping and pivoting
o Data transformation
o String Manipulation
6.) Model Development and Evaluation
o Introduction to machine learning
o Supervised
o Unsupervised Learning
o Model development using Linear Regression
o Model Visualization
o Prediction and Decision Making
o Model Evaluation
o Over-fitting
o Under-fitting and Model Selection
Location
Vivek Nagar Rudrapur Udham Singh Nagar Uttarakhand(263153), Udham Singh Nagar, Dehradun, Uttarakhand
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