Why is coding important for kids?

Coding boosts creativity, problem-solving, and tech skills, preparing kids for future careers in AI, robotics, and more. Mero Coding Class makes learning fun and accessible for all ages.

Which coding language is best?

The best coding language depends on your goals. For beginners, especially kids, Scratch is a great start as it introduces programming concepts through visual blocks. For more advanced learning, Python is recommended due to its simplicity and wide use in fields like web development and AI. At Mero Coding Class, we offer courses in Scratch, Python and more to help you get started.

Which coding language should I learn first?

If you're new to coding, Scratch is ideal, especially for kids, as it teaches the basics in a fun, interactive way. For older learners, Python is a great first text-based language due to its simple syntax. Mero Coding Class provides beginner-friendly courses in both Scratch and Python to guide your learning journey.

How many coding language are there?

There are hundreds of coding languages, each with specific uses. Popular ones include Python, JavaScript, and Java. For beginners, Scratch and Blockly are excellent choices. Mero Coding Class offers access to these beginner-friendly languages.

Can coding be self taught?

Yes, coding can be self-taught using online resources like Code.org and Scratch. At Mero Coding Class, we provide structured, self-paced courses to help you learn coding independently, with plenty of support along the way.

Start with platforms like Scratch to learn basic programming logic. Progress to text-based languages like Python. Use resources like Code.org or join Mero Coding Class for structured courses and interactive exercises. Consistent practice is key.

At what age should kids start learning coding?

Kids can start coding as early as 8 with Scratch, which makes learning easy and fun. Teens can progress to Python and robotics. Mero Coding Class provides coding courses for grades 1 to 10, suited to all skill levels.

AI | Mero Coding Class

About Course

Target Audience: Students who have passed grade 10

Level: Beginner to Intermediate

Duration: 50 Classes, 2 Hour Each

Mode of Delivery: Online or in-person, hands-on learning.

Prerequisites: Basic understanding of Computer and its terms..

Teaching Approach:

- Interactive lessons with step-by-step guidance.
- Project-based learning to reinforce concepts.
- Hands-on activities and real-world case studies.
- Collaboration and peer feedback to enhance understanding.

Why this Course?

Introduction to Artificial Intelligence (AI)
Machine Learning Fundamentals
Data Preprocessing
Building Machine Learning Models
Deep Learning Basics
Hands-On Projects
Evaluating AI Models

Course Content

₨25,000

Skill LevelIntermediate
LanguagePython
Course Duration: 30h

AI

₨25,000 ₨25,000

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Why this Course?

Course Content

Target Audience

AI

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AI

About Course

Why this Course?

Course Content

Class 1: Introduction to AI and Its Applications Goal: Understand AI fundamentals and real-world applications. Activity: Interactive lecture and real-world case studies. AI demo video and class discussion on ethical implications.

Class 2: Introduction to Python, Variables, Data types and Operators Goal: Learn Python basics and data types. Activity: Writing and executing Python scripts. Hands-on exercises with data types and operators.

Class 3: Control Flow Statements Goal: Understand conditional statements and loops. Activity: Writing Python scripts for decision-making and iterations.

Class 4: Functions and Modules Goal: Learn modular programming with functions. Activity: Writing modular Python programs.

Class 5: Data Structures (Lists, Tuples, Dictionaries, Sets) Goal: Learn Python data structures. Activity: Implementing real-world problems using data structures.

Class 6: Importing Libraries and Working with NumPy Goal: Learn array operations using NumPy. Activity: Performing matrix operations using NumPy.

Class 7: Data Handling with Pandas Goal: Work with datasets using Pandas. Activity: Hands-on tasks for handling datasets.

Class 8: Data Visualization with Matplotlib Goal: Visualizing data using charts. Activity: Plotting datasets with Matplotlib.

Class 9: File Handling & Regular Expressions Goal: Learn file operations and pattern matching. Activity: Writing scripts for file processing and text extraction.

Class 10: Introduction to Scikit-Learn Goal: Learn basic ML workflows with Scikit-Learn. Activity: Implementing a simple ML workflow.

Class 11: Advanced Scikit-Learn Goal: Learn feature scaling and model evaluation. Activity: Training and testing ML models.

Class 12: Intro & Setting Up ML Environment Goal: Understand the ML ecosystem. Activity: Installing ML libraries and exploring demo projects.

Class 13: Understanding ML Workflow Goal: Learn ML pipeline steps. Activity: Building an end-to-end ML pipeline using a dataset.

Class 14: Linear Regression Goal: Learn how Linear Regression works for predicting continuous values. Activity: Implementing Linear Regression using Scikit-Learn Predicting house prices with a real dataset

Class 15: Logistic Regression Goal: Understand Logistic Regression for binary classification tasks. Activity: Implementing Logistic Regression using Scikit-Learn Hands-on project (e.g., predicting customer churn, email spam detection) Evaluating models using classification metrics

Class 16: Decision Trees and Random Forest Goal: Learn how Decision Trees and Random Forest work for classification. Activity: Building classification models using Decision Trees and Random Forest Evaluating model performance with real datasets

Class 17: Introduction to Support Vector Machines (SVM) Goal: Learn how SVM works for classification tasks. Activity: Implementing SVM using Scikit-Learn Hands-on practice with binary classification datasets

Class 18: Model Training and Evaluation Goal: Understand how to train and evaluate ML models effectively. Activity: Tuning hyperparameters and validating models Exploring evaluation metrics with practical examples

Class 19: Hands-on Supervised Learning Project Goal: Apply supervised learning techniques to a real-world problem. Activity: Developing and training a model Project feedback and discussion on challenges faced

Class 20: Introduction to Unsupervised Learning & Clustering Techniques Goal: Understand the basics of unsupervised learning and clustering. Activity: Exploring clustering algorithms: K-Means, Hierarchical Clustering, DBSCAN Hands-on clustering with real-world datasets (e.g., customer segmentation)

Class 21: K-Means Clustering Goal: Learn and apply K-Means clustering effectively. Activity: Implementing K-Means on datasets (e.g., customer segmentation, market segmentation) Experimenting with different cluster numbers and evaluating results

Class 22: Hierarchical Clustering & DBSCAN Goal: Explore Hierarchical Clustering and DBSCAN for different clustering tasks. Activity: Implementing Hierarchical Clustering and DBSCAN on datasets Visualizing dendrograms and experimenting with DBSCAN for noisy data

Class 23: Dimensionality Reduction with PCA Goal: Learn how to reduce the dimensionality of data using PCA. Activity: Applying PCA for feature reduction on datasets Visualizing high-dimensional data using 2D or 3D projections post-PCA Discussing PCA’s role in improving algorithm performance

Class 24: Association Rule Learning Goal: Explore the concept of association rule learning for market basket analysis. Activity: Implementing the Apriori Algorithm for market basket analysis Analyzing transaction data to find frequent itemsets and association rules

Class 25: Anomaly Detection Goal: Understand anomaly detection techniques for identifying outliers. Activity: Implementing anomaly detection algorithms (Isolation Forest, One-Class SVM) Hands-on project for detecting fraud in financial transactions or network anomalies

Class 32: Feature Extraction & Word Embeddings Goal: Learn how to convert text into numerical representations and explore word embeddings. Activity: Implementing BoW and TF-IDF in Scikit-Learn Training and visualizing word embeddings using Word2Vec (Gensim)

Class 33: Sentiment Analysis & Text Classification Goal: Build models for sentiment analysis and text classification using supervised learning techniques. Activity: Building a sentiment analysis model using Scikit-Learn Classifying tweets, product reviews, or news articles

Class 34: Named Entity Recognition (NER) & Topic Modeling Goal: Learn to extract entities from text and apply topic modeling techniques. Activity: Implementing NER using spaCy on real-world text Performing topic modeling using LDA in Gensim

Class 35: Chatbots and NLP Model Deployment Goal: Understand chatbot development and deployment of NLP models in real-world applications. Activity: Implementing a chatbot using Dialogflow or Python NLP libraries Deploying an NLP model as an API

Class 36: Hands-on NLP Project & Review Goal: Apply NLP techniques in real-world projects and review the concepts learned. Activity: Working on a real-world NLP project (e.g., fake news detection, document summarization, chatbot) Final review and Q&A session

Class 37: Introduction to Reinforcement Learning (RL) Goal: Understand the fundamentals of reinforcement learning and its applications. Activity: Interactive lecture with real-world RL applications (game playing, robotics, autonomous vehicles) Setting up OpenAI Gym for RL environments

Class 46: Car Price Prediction Goal: Predict car prices using machine learning algorithms. Activity: Building a regression model to predict car prices using datasets Evaluating the model’s performance Creating a user-friendly Streamlit app for inputting car details and predicting its price

Class 49: Deploying AI Models Goal: Learn how to deploy machine learning models and explain their decisions in real-world applications. Activity: Deploying a machine learning model on the cloud (AWS, GCP, or Azure) Integrating model interpretability methods like SHAP to explain AI decisions

Class 50: Ethics in AI Goal: Understand the ethical challenges in AI and how to address them. Activity: Case studies on ethical issues in AI (e.g., biased hiring algorithms, facial recognition) Group discussions on how to address bias and fairness in AI development

Target Audience

AI