Ajai Krishna
Bangalore/Online
Skills
K-means Clustering, Ml Dl Algorithms, Logistic Regression
Python for Ml and Data Analysis
Python Basics
Numpy, Pandas and Matplotlib
Machine Learning
Deep Learning
Supervised Learning
Deep Learning Frameworks (tensorflow, Pytorch, Keras)
Data Science
Data Visualization
Data Cleaning
Python Programming
Python for Data Science
About
I am a Data Science professional with experience in Python, data analysis, and machine learning. I have worked with real-world datasets and practical projects, which helps me teach...
I am a Data Science professional with experience in Python, data analysis, and machine learning. I have worked with real-world datasets and practical projects, which helps me teach concepts in a clear and structured way.I focus on building strong fundamentals in Python, statistics, and data analysis before moving to machine learning and project implementation. My sessions are practical and hands-on, so students can understand not just the theory but also how to apply it.I aim to support students at different levels — from beginners learning Python to those working on data science and ML projects — and help them gain confidence through consistent guidance and practice.
Ajai Krishna
Bangalore/Online
Skills :
K-means Clustering, Ml Dl Algorithms, Logistic Regression
Python for Ml and Data Analysis
Python Basics
Numpy, Pandas and Matplotlib
Machine Learning
Deep Learning
Supervised Learning
Deep Learning Frameworks (tensorflow, Pytorch, Keras)
Data Science
Data Visualization
Data Cleaning
Python Programming
Python for Data Science
I am a Data Science professional with experience in Python, data analysis, and machine learning. I have worked with real-world datasets and practical projects, which helps me teach...
I am a Data Science professional with experience in Python, data analysis, and machine learning. I have worked with real-world datasets and practical projects, which helps me teach concepts in a clear and structured way.I focus on building strong fundamentals in Python, statistics, and data analysis before moving to machine learning and project implementation. My sessions are practical and hands-on, so students can understand not just the theory but also how to apply it.I aim to support students at different levels — from beginners learning Python to those working on data science and ML projects — and help them gain confidence through consistent guidance and practice.
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Courses by: Ajai Krishna
Data Science & Machine Learning Course by Ajai Krishna
The Data Science & Machine Learning Mastery Course is a comprehensive, three-month program designed to equip students and professionals with the skills needed to analyze data, build machine learning models, and develop deep learning applications. The course combines hands-on programming, statistical analysis, data visualization, and advanced AI techniques to provide a complete learning experience.
This program is ideal for beginners, intermediate learners, and professionals who want to enter the growing field of data science and machine learning. By the end of the course, learners will gain practical experience in Python programming, data manipulation, statistical modeling, machine learning algorithms, and real-world project development. Both online and offline modes ensure flexibility for learners across locations.
What Students Will Learn
Month 1: Python & Data Fundamentals
Week 1: Python Basics
Introduction to Python
Variables, Data Types
Operators
Conditional Statements
Loops
Functions
Week 2: Data Structures
Lists, Tuples, Sets
Dictionaries
String Handling
File Handling
Basic Problem Solving
Week 3: NumPy & Pandas
Introduction to NumPy
Arrays and Operations
Introduction to Pandas
Series and DataFrames
Data Cleaning
Week 4: Data Visualization & EDA
Matplotlib
Seaborn
Exploratory Data Analysis (EDA)
Handling Missing Data
Mini Project (Data Analysis)
Month 2: Statistics & Machine Learning
Week 5: Statistics for Data Science
Mean, Median, Mode
Variance & Standard Deviation
Probability Basics
Correlation
Distributions
Week 6: Data Preprocessing
Feature Engineering
Encoding Categorical Data
Scaling & Normalization
Train-Test Split
Week 7: Supervised Learning
Linear Regression
Logistic Regression
K-Nearest Neighbors
Model Evaluation Metrics
Week 8: Advanced Supervised Models
Decision Trees
Random Forest
Support Vector Machine
Mini ML Project
Month 3: Advanced Topics & Projects
Week 9: Unsupervised Learning
K-Means Clustering
Hierarchical Clustering
PCA (Dimensionality Reduction)
Week 10: Introduction to Deep Learning
Neural Network Basics
ANN Concept
Introduction to TensorFlow/Keras
Week 11: Real-World Applications
End-to-End ML Workflow
Deployment Basics
Working with Real Datasets
Week 12: Capstone Project
Problem Statement Selection
Data Collection & Cleaning
Model Building
Final Presentation
Teaching Method
The course is offered in online and offline modes, with an emphasis on interactive and hands-on learning. The teaching approach includes:
• Step-by-step Python programming and ML concept explanation
• Practical exercises, coding demonstrations, and real datasets
• Guided mini projects and capstone project for hands-on experience
• Interactive Q&A sessions and personalized support
• Exposure to industry-relevant tools and frameworks
Students gain both conceptual understanding and practical implementation skills to handle real-world data science challenges.
Why This Course
This course provides a structured learning pathway from beginner to advanced topics, combining Python programming, data analysis, machine learning, and deep learning. The inclusion of mini projects and a capstone ensures practical experience, while both online and offline modes provide flexibility. The curriculum is designed to help learners confidently apply data science skills in academic, professional, or research settings.
Benefits and Outcomes
By completing this course, students will:
• Master Python programming for data analysis and machine learning
• Develop expertise in data cleaning, visualization, and preprocessing
• Understand supervised and unsupervised machine learning algorithms
• Gain foundational knowledge in deep learning frameworks such as TensorFlow and Keras
• Build a portfolio with mini projects and a capstone project
• Enhance problem-solving, analytical, and data-driven decision-making skills
• Prepare for careers in data science, AI, and machine learning
This course provides a complete, hands-on learning experience for anyone looking to excel in the field of data science and machine learning.
