Rishab - Machine learning tutor - Charlotte
Rishab - Machine learning tutor - Charlotte

Rishab profile and its contact details have been verified by our experts

Rishab

  • Rate R248
  • Response 1h
  • Students

    Number of students Rishab has accompanied since joining Superprof

    7

    Number of students Rishab has accompanied since joining Superprof

Rishab - Machine learning tutor - Charlotte
  • 5 (5 reviews)

R248/h

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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 tutor in our community. He is highly recommended for his commitment and the quality of his lessons. A trusted partner on your learning journey.

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
  • Matric/GCSE
  • +12
  • levels :

    Primary

    Secondary

    Matric/GCSE

    AS Level

    A Level

    BTech

    Adult education

    Masters

    Doctorate

    MBA

    Beginner

    Intermediate

    Advanced

    Professional

    Kids

  • English

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.
______________________________________
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.
______________________________________
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.
______________________________________
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.
______________________________________
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.
______________________________________
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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Rates

Rate

  • R248

Package rates

  • 5h: R1240
  • 10h: R2480

online

  • R248/h

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