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Rishab
- Rate 168 GHS
- Response 1h
-
Students7
Number of students Rishab has accompanied since arriving at Superprof
Number of students Rishab has accompanied since arriving at Superprof
168 GHS/hr
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- Machine learning
Explore 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
Recommended
Rishab is a respected member of our tutor community. He is highly recommended for his commitment and the quality of his lessons. An excellent choice to progress with confidence.
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 school
- Junior high school
- SHS 1
- +14
levels :
Primary school
Junior high school
SHS 1
SHS 2
SHS 3
BTS
University education
Adult Education
Master's degree
Doctor of philosophy
MBA
Pre school
Beginner
Intermediate
Advanced
Professionel
Child
- 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
5
Rishab was VERY helpful in teaching me how to edit code in a professional manner. I recommend him for any coding help you need! Thanks again Rishab!
He's unique in his way, as he finds different methods solve problems in the source code. His, this unique technique helped me in analysing problems, build algorithms to solve them on my own.
Yes, join with him. He's worth it!!Rishab is a like mould through which even a weak beginner will get moulded into a strong expert in computer languages. In my leisure time, I get to learn new programming concepts from him!
You too should have an experience of learning with RishabYou'll have the best experience of learning coding from Rishab. He's sharp at his method I'd say. I got to learn a tonne of things from him.
Thanks Rishab....To start with, he is a very good computer tutor. At a very young age, he has chosen a right path and started to work on it. Though he is young, but the way he explains and teaches the concepts is nevertheless similar to a professional teacher. Yes! you have searched the right person for you...
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Rates
Rate
- 168 GHS
Pack prices
- 5h: 840 GHS
- 10h: 1680 GHS
online
- GHS168/h
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