Leenu Thomas Data science and Machine Learning Trainer
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Hi Students,
Passionate to teach Data science, Machine Learning, and Deep Learning. Completed certification in Applied Data Science from IIT Palakkad. Did internships and working in the field of Data Science. Completed master's in computer application from CUSAT with CGPA 7.78. Graduation in Mathematics (MG University) with 76%. Able to explain concepts in English, Hindi, and Malayalam. Three years of teaching experience as a private tutor. I want to teach Data Science and Machine Learning with many practice sections to make them understand concepts very well. Regular practice using different types of data will be provided. Will evaluate every project.
Thank you.

Subjects

  • NLP basics Beginner-Expert

  • Machine learning Python Beginner-Expert

  • Data Science with Python Beginner-Expert

  • Machine learning projects Beginner-Intermediate

Experience

  • Data Science Trainer (May, 2024–Present) at Prewatech
    As a Data Science Trainer, I am responsible for designing, delivering, and updating comprehensive training programs in data science, including topics such as data analysis (using Python), machine learning, and deep learning. Mentor students and provide support to ensure they acquire the necessary skills to succeed in the field of data science.
  • jr. Data scientist (Jul, 2023–Feb, 2024) at Wahy lab solution Kochi
  • Programmer (Aug, 2011–Dec, 2013) at Acty system india pvt ltd
    Worked as a programmer for 2 years. In the C# domain. Mainly worked on destop applications.

Education

  • Applied Datascience (Jun, 2022–Jan, 2023) from Iit palakkad
  • Masters in computer application (Jul, 2008–Apr, 2011) from Cochin university of science and technology, CUSAT–scored Cgpa 7.8
  • Bsc. Mathematics (Aug, 2005–Mar, 2008) from St.aloysius, Edathua–scored 76 %

Fee details

    ₹200–500/hour (US$2.38–5.95/hour)

Courses offered

  • Machine Learning

    • ₹15000
    • Duration: 6 Months
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Hindi, Malayalam
    • Certificate provided: No
    1. Introduction to Machine Learning
    What is Machine Learning?
    Types of Machine Learning: Supervised, Unsupervised, Semi-supervised, Reinforcement Learning
    Applications of Machine Learning
    Machine Learning Workflow: Data Collection, Data Preparation, Model Selection, Training, Evaluation, Deployment
    2. Data Preprocessing
    Understanding Data: Types of Data, Features, Labels
    Handling Missing Data: Imputation Techniques
    Data Normalization and Standardization
    Feature Engineering and Feature Selection
    Data Splitting: Train/Test/Validation Sets
    3. Supervised Learning
    Regression
    Linear Regression
    Polynomial Regression
    Ridge and Lasso Regression
    Evaluation Metrics: MSE, RMSE, R²
    Classification
    Logistic Regression
    k-Nearest Neighbors (k-NN)
    Support Vector Machines (SVM)
    Decision Trees
    Random Forests
    Gradient Boosting Machines (GBM, XGBoost)
    Evaluation Metrics: Accuracy, Precision, Recall, F1 Score, AUC-ROC
    4. Unsupervised Learning
    Clustering
    k-Means Clustering
    Hierarchical Clustering
    DBSCAN
    Dimensionality Reduction
    Principal Component Analysis (PCA)
    Linear Discriminant Analysis (LDA)
    t-Distributed Stochastic Neighbor Embedding (t-SNE)
    Association Rule Learning
    Apriori Algorithm
    Eclat Algorithm
    5. Ensemble Methods
    Bagging and Bootstrap Aggregating
    Boosting (AdaBoost, Gradient Boosting, XGBoost)

  • Data science

    • ₹20000
    • Duration: 6 Months
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Malayalam, Hindi
    • Certificate provided: No
    1. Introduction to Data Science
    What is Data Science?
    The Data Science Process
    Roles in Data Science (Data Analyst, Data Scientist, Data Engineer)
    Applications of Data Science
    2. Python for Data Science
    Python Basics: Variables, Data Types, Control Structures
    Libraries for Data Science: NumPy, Pandas, Matplotlib, Seaborn
    Data Manipulation with Pandas
    Data Visualization with Matplotlib and Seaborn
    3. Statistics and Probability
    Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
    Probability Theory: Probability Distributions, Bayes’ Theorem
    Hypothesis Testing: t-tests, Chi-square test, p-values
    Confidence Intervals and Significance Levels
    4. Data Exploration and Preparation
    Data Cleaning: Handling Missing Values, Outliers
    Data Transformation: Scaling, Normalization, Encoding Categorical Variables
    Feature Engineering: Creating New Features, Feature Selection
    Exploratory Data Analysis (EDA): Univariate, Bivariate, and Multivariate Analysis
    5. Machine Learning Fundamentals
    Introduction to Machine Learning: Supervised vs Unsupervised Learning
    Regression Analysis: Linear Regression, Polynomial Regression
    Classification Algorithms: Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests
    Model Evaluation: Accuracy, Precision, Recall, F1 Score, ROC-AUC
    6. Advanced Machine Learning
    Support Vector Machines (SVM)
    Ensemble Learning: Bagging, Boosting, Stacking
    Clustering Algorithms: K-Means, Hierarchical Clustering, DBSCAN
    Dimensionality Reduction: PCA, t-SNE

  • Power BI Data Analyst course

    • ₹5000
    • Duration: 2 Months
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Hindi, Malayalam
    • Certificate provided: No
    1. Introduction to Power BI
    Overview of Business Intelligence (BI)
    What is Power BI?
    Components of Power BI: Power BI Desktop, Power BI Service, Power BI Mobile
    Power BI Workflow: Data Import, Data Modeling, Visualization, and Sharing
    2. Getting Started with Power BI Desktop
    Installing Power BI Desktop
    Power BI Interface Overview
    Connecting to Data Sources: Excel, CSV, SQL Server, Web Data, etc.
    Data Loading and Data Transformation in Power Query Editor
    3. Data Modeling in Power BI
    Understanding Tables, Fields, and Relationships
    Creating Relationships between Tables
    Data Types and Formatting
    Calculated Columns and Measures
    Creating and Using DAX (Data Analysis Expressions) in Power BI
    4. Data Transformation with Power Query
    Introduction to Power Query
    Data Cleaning: Removing Duplicates, Handling Missing Values, Filtering Data
    Data Transformation: Merging, Appending, Grouping, Pivoting, and Unpivoting Data
    Advanced Data Transformation Techniques
    Custom Functions and Conditional Columns
    5. Data Visualization and Reporting
    Creating Basic Visuals: Bar Charts, Line Charts, Pie Charts, etc.
    Working with Slicers and Filters
    Creating Interactive Dashboards
    Customizing Visuals: Formatting, Conditional Formatting, Themes
    Using Advanced Visuals: Maps, Gauges, KPIs, TreeMaps, etc.
    Creating and Managing Report Pages
    6. Advanced Data Analysis with DAX
    Introduction to DAX: Syntax and Functions
    Common DAX Functions: SUM, AVERAGE, COUNTROWS, etc.
    Time Intelligence Functions: Year-to-Date, Month-to-Date, Running Totals
    Creating Calculated Tables and Columns
    Working with DAX for Complex Calculations and Measures
    7. Power BI Service
    Publishing Reports to Power BI Service
    Creating and Managing Workspaces
    Sharing and Collaborating on Reports
    Setting Up Data Refresh and Schedules
    Using Power BI Mobile

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