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Artificial intelligence and machine learning

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Artificial intelligence and machine learning

Classification (Supervised Learning)

  • What is Classification?
  • Finding Patterns/Fixed Patterns
  • Problems with Fixed Patterns
  • Machine learning approach over fixed pattern approach
  • Decision Tree based classification
  • Ensemble Based Classification
  • Logistic Regression (SGD Classifier)
  • Accuracy measurements
  • Confusion Matrix
  • ROC Curve
  • AUC Score
  • Multi-class Classification
  • Softmax Regression Classifier
  • Multi-label Classification
  • Multi-output Classification.

Ensemble models

  • Random Forest
  • Bagging
  • Boosting
  • Adaptive Boosting
  • Gradient Boosting
  • Extreme Gradient Boosting
  • Heterogeneous Ensemble Models
  • Stacking

Multiple/Polynomial Regression (scikit-learn)

  • Multiple Linear Regressions (SGD Regressor)
  • Gradient Descent (Calculus way of solving linear equation)
  • Feature Scaling (Min-Max vs Mean Normalization)
  • Feature Transformation
  • Polynomial Regression
  • Matrix addition, subtraction, multiplication and transpose
  • Optimization theory for data scientist.

Optimization Theory (Gradient Descent Algorithm)

  • Modelling ML problems with optimization requirements
  • Solving unconstrained optimization problems
  • Solving optimization problems with linear constraints
  • Gradient descent ideas
  • Gradient descent
  • Batch gradient descent
  • Stochastic gradient descent.

Model Evaluation and Error Analysis

  • Train/Validation/Test split
  • K-Fold Cross Validation
  • The Problem of Over-fitting (Bias-Variance tread-off)
  • Learning Curve
  • Regularization (Ridge, Lasso and Elastic-Net)

Recommendation Problem

  • What is Recommendation System?
  • Top-N Recommender
  • Rating Prediction
  • Content based Recommenders
  • Limitations of Content based recommenders
  • Machine Learning Approaches for Recommenders.
  • User-User KNN model, Item-Item KNN model
  • Factorization or latent factor model
  • Hybrid Recommenders
  • Evaluation Metrics for Recommendation Algorithms
  • Top-N Recommnder : Accuracy, Error Rate
  • Rating Prediction: RMSE.

Clustering (Unsupervised Learning)

  • Finding pattern and Fixed Pattern Approach
  • Limitations of Fixed Pattern Approach
  • Machine Learning Approaches for Clustering
  • Iterative based K-Means Approaches
  • Density based DB-SCAN Approach
  • Evaluation Metrics for Clustering
  • Cohesion, Coupling Metrics
  • Correlation Metric.

Support Vector Machine (SVM)

  • SVM Classifier (Soft/Hard – Margin)
  • Linear SVM
  • Non-Linear SVM
  • Kernel SVM
  • SVM Regression.

PCA (Unsupervised Learning)

  • Dimensionality Reduction
  • Choosing Number of Dimensions or Principal Components
  • Incremental PCA
  • Kernel PCA
  • When to apply PCA?
  • Eigen vectors
  • Eigen values.

Model Deployment

  • Pickle (Pkl file)
  • Model load from Pkl file and prediction.

Association Rules

  • A priori Algorithm
  • Collaborative Filtering (User-Item based)

PCA (Unsupervised Learning)

  • Dimensionality Reduction
  • Choosing Number of Dimensions or Principal Components
  • Incremental PCA
  • Kernel PCA
  • When to apply PCA?
  • Eigen vectors
  • Eigen values.

What is python ?

Python is a general-purpose programming language, which means that, unlike JavaScript, HTML, and CSS, it can be used in applications beyond web development. Though it’s been around for 30 years, it has recently become one of the most popular programming languages, and its popularity continues to grow.

What You Can Do With AI

Artificial intelligence is a constellation of many different technologies working together to enable machines to sense, comprehend, act, and learn with human-like levels of intelligence. Maybe that’s why it seems as though everyone’s definition of artificial intelligence is different: AI isn’t just one thing.

General AI is more like what you see in sci-fi films, where sentient machines emulate human intelligence, thinking strategically, abstractly and creatively, with the ability to handle a range of complex tasks. While machines can perform some tasks better than humans (e.g. data processing), this fully realized vision of general AI does not yet exist outside the silver screen. That’s why human-machine collaboration is crucial—in today’s world, artificial intelligence remains an extension of human capabilities, not a replacement.

End-to-end efficiency: AI eliminates friction and improves analytics and resource utilization across your organization, resulting in significant cost reductions. It can also automate complex processes and minimize downtime by predicting maintenance needs. Improved accuracy and decision-making: AI augments human intelligence with rich analytics and pattern prediction capabilities to improve the quality, effectiveness, and creativity of employee decisions. Intelligent offerings: Because machines think differently from humans, they can uncover gaps and opportunities in the market more quickly, helping you introduce new products, services, channels and business models with a level of speed and quality that wasn’t possible before.

In AI and ML, Python is a dominant language. Its simplicity and extensive libraries, including TensorFlow and PyTorch, facilitate the development, training, and deployment of machine learning models. Python's versatility supports tasks like natural language processing, computer vision, and reinforcement learning, making it the preferred choice for AI and ML practitioners. Popular frameworks, community support, and a rich ecosystem contribute to Python's prominence in creating sophisticated and scalable artificial intelligence solutions.

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    Frequently Asked Question

    AI in data science automates complex tasks like pattern recognition and predictive modeling. Machine learning algorithms analyze vast datasets to uncover insights, make predictions, and optimize decision-making. AI enhances data processing efficiency, enables advanced analytics, and supports data-driven decision-making, revolutionizing the field of data science.

    In general programming, we have the data and the logic by using these two we create the answers. But in machine learning, we have the data and the answers and we let the machine learn the logic from them so, that the same logic can be used to answer the questions which will be faced in the future. Also, there are times when writing logic in codes is not possible so, at those times machine learning becomes a saviour and learns the logic itself

    "With our data science course, you'll master essential skills in Python, power BI and machine learning. Analyse data, create insightful visualizations, and apply statistical techniques. Gain hands-on experience with real-world projects, fostering critical problem-solving skills. Acquire the knowledge to excel in diverse data-driven roles and drive innovation in any industry.

    Python is known for its simplicity, readability, and versatility. It supports object-oriented, imperative, and functional programming styles. Its extensive standard library facilitates diverse tasks. Dynamic typing and automatic memory management enhance development speed. Python is widely used for web development, data science, machine learning, and automation.

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