Rishab's profile, qualifications and contact details have been verified by our experts
Rishab
- Rate RM38
- Response 3h
-
Students50+
Number of students accompanied by Rishab since their arrival at Superprof
Number of students accompanied by Rishab since their arrival at Superprof
RM38/h
Contact
- Machine learning
Master Practical Machine Learning and AI from basic Supervised & Unsupervised Learning, to advanced Neural Networks, and Data Science Concepts for Real-World Problem Solving!
- Machine learning
Lesson location
Super tutor
Rishab is one of our best Machine learning tutors. High-quality profile, verified qualifications, a quick response time, and great reviews from students!
About Rishab
I'm a computer science undergrad. I started my coding career when I was 12! Age is not a barrier for gaining knowledge, that's what I believe. I learned AI-ML concepts in just 2 months! You too can do so, just join me!!
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About the lesson
- Primary
- Secondary
- SPM
- +13
levels :
Primary
Secondary
SPM
Form 6
STPM
Adult education
Bachelor
Masters
Diploma
Doctorate
Other
Beginner
Intermediate
Advanced
Professional
Kids
- English
All languages in which the lesson is available :
English
1. Introduction to Artificial Intelligence and Machine Learning
1.1. Overview of AI & ML
• What is AI? Types of AI: Narrow vs. General AI.
• The evolution of Machine Learning.
• Key concepts in AI: Intelligent agents, search, problem-solving.
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1.2. Types of Machine Learning
• Supervised Learning: Definition, Use cases.
• Unsupervised Learning: Clustering and association.
• Reinforcement Learning: Introduction and use cases.
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1.3. Setting up the Python Environment
• Installing libraries: NumPy, Pandas, Matplotlib, Scikit-learn.
• Introduction to Jupyter Notebooks & Google Colab.
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2. Data Preprocessing and Feature Engineering
2.1. Data Cleaning & Transformation
• Handling missing data, data imputation techniques.
• Encoding categorical data, scaling features.
• Feature extraction and selection techniques.
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2.2. Data Visualization
• Visualizing data using Matplotlib, Seaborn.
• Exploratory Data Analysis (EDA) best practices.
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2.3. Case Study: EDA on a real-world dataset.
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3. Supervised Learning Techniques
3.1. Regression Models
• Linear Regression: Theory, implementation, evaluation metrics.
• Polynomial Regression, Ridge, and Lasso Regression.
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3.2. Classification Models
• Logistic Regression, K-Nearest Neighbors (KNN).
• Decision Trees, Random Forests, Support Vector Machines (SVM).
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3.3. Model Evaluation
• Cross-validation, bias-variance tradeoff.
• Metrics: Accuracy, Precision, Recall, F1-score, ROC, and AUC.
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3.4. Case Study: Building a classifier for real-world data
• Example: Loan approval, image classification.
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4. Unsupervised Learning and Clustering
4.1. Clustering Algorithms
• K-means Clustering, DBSCAN, Hierarchical Clustering.
• Dimensionality Reduction: PCA (Principal Component Analysis), t-SNE.
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4.2. Association Algorithms
• Apriori, Eclat for market basket analysis.
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4.3. Case Study: Building a customer segmentation model.
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5. Deep Learning and Neural Networks
5.1. Introduction to Neural Networks
• Neurons and layers, activation functions (Sigmoid, ReLU, Softmax).
• Forward and backward propagation.
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5.2. Deep Learning Models
• Convolutional Neural Networks (CNN) for computer vision.
• Recurrent Neural Networks (RNN) for time series and NLP.
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5.3. Deep Learning Frameworks: Keras
• Implementing a basic neural network with Keras.
• Model optimization: Adam, SGD, and learning rate tuning.
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5.4. Case Study: Image classification with CNNs, time-series forecasting with RNNs.
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6. Advanced Topics in Machine Learning
6.1. Reinforcement Learning
• Introduction to Q-Learning, policy gradients, and Markov Decision Processes (MDPs).
• Applications in game playing (e.g., AlphaGo).
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6.2. Transfer Learning
• Using pre-trained models in deep learning (e.g., VGG, ResNet).
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6.3. Natural Language Processing (NLP)
• Tokenization, Text preprocessing.
• Bag-of-Words, Word2Vec, and Transformers.
• Implementing a basic sentiment analysis model.
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6.4. Generative Models
• GANs (Generative Adversarial Networks).
• Variational Autoencoders (VAEs).
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6.5. Case Study: Building an AI agent using reinforcement learning.
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Recommendations
Recommendations come from relatives, friends and acquaintances of the teacher
8
Sir teaches, every concept in depth with various on live going examples which makes the class very much interesting and worth fullness. He is very interesting and supercool.
It's interesting and interactive to learn Programming Languages from Rishab you know! He taught me the fundamentals of HTML CSS JavaScript in just a couple of weeks. You will be missing his classes once you finish your course, so do I...
He is an well knowledgeable guy who knows to teach in an very good manner even I got many things to know from him
His behaviour is very polite and he always tries to cover the whole information about the concept which he is explaining . So, overall he is a very good teacher
Rishab is a very great student. The way he conducts online classes is awesome. He clearly explains every topic.
Yes!! Sure he is The way is interact with people is Good.He is decent and has a positive attitude.I hope he do the same in future
The way he talks n explains can get to know is point very easily in short we can say he is an good teacher.
He is excellent and teaches with joy which makes the class interesting and exciting.
He makes through the topic thoroughly and explains it perfectly such that we can understand the topic very well.View more recommendations
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Rates
Rate
- RM38
Pack rates
- 5h: RM190
- 10h: RM380
online
- RM38/h
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