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Machine Learning

Machine learning is a pivotal component of data science, empowering the extraction of insights from vast datasets. It encompasses algorithms and techniques enabling systems to learn patterns, make predictions, and automate decision-making without explicit programming. 

Supervised learning involves training models on labeled data for prediction, while unsupervised learning discovers inherent structures in unlabeled data. Reinforcement learning aids in optimizing actions through trial and error. Deep learning, a subset, employs neural networks for complex tasks like image and speech recognition. 

Feature engineering, model selection, and evaluation are crucial stages. Python libraries like TensorFlow, Scikit-learn, and PyTorch facilitate implementation. However, careful consideration of data quality, model interpretability, and ethical concerns are vital for effective and responsible machine learning in data science.

What you will learn?

  • Python
  • APIs
  • Databases
  • Python projects
  • Numpy
  • Pandas
  • Visualizations
  • Stats
  • Supervised Machine learning Algorithms
  • Unsupervised Machine learning Algorithms
  • Dimensionality Reduction
  • Machine Learning Projects
  • Deep learning
  • PowerBI
  • Tableau
  • Introduction of machine learning
  • Difference between Supervised, Unsupervised & Semi-supervised
  • Linear Regression Mathematical Institution
  • Linear Regression assumption.
  • OLS
  • Different Training methodology
  • Train, Test, Validation Split
  • Hands-on linear regression in python from scratch
  • Complete hands-on with scikit learn
  • Overfitting & Underfitting
  • Ridge Regression
  • Lasso Regression
  • Elastic Net Regression
  • Polynomial Regression
  • Logistics regression
  • Difference between Linear Regression and Logistic Regression
  • Performance matrix
  • Confusion matrix
  • Precision, Recall, ROC, AUC Curve
  • F-beta Score
  • SVR(support vector regressor)
  • SVC(support vector classifier)
  • SVM(Support vector machine)
  • KNN Classifier
  • KNN Regressor
  • K Nearest Neighbour
  • Lazy learners
  • KNN Issues
  • Performance measurement of KNN
  • Decision Tree Classifier
  • Decision tree Regressor
  • Cross Validation
  • Bias vs Variance
  • Ensemble approach
  • Bagging
  • Boosting
  • Stacking
  • Random Forest
  • Ada boosting
  • Gradient boosting
  • XGBoosting
  • Hands-on XgBoost
  • Introduction to K-Means Clustering
  • Hard K-Means clustering
  • Soft K-Means clustering
  • Visualizing Each Step of K-Means
  • How to Choose K value
  • Advantages and Disadvantages of K-Means Clustering
  • Examples of where K-Means can fail
  • How to Evaluate a Clustering algorithm
  • Silhouette Coefficient
  • Dunn’s Index
  • Python implementation using K-Means on Real Data
  • Real-time Clustering Application
  • Visual Walkthrough of Agglomerative Hierarchical Clustering
  • Using Hierarchical Clustering in Python and Interpreting the Dendrogram
  • python implementation of Agglomerative Clustering
  • DBSCAN: A Density-Based Clustering Algorithm
  • How to use DBSCAN: A Density-Based Clustering Algorithm for outlier detection
  • Python implementation of DBSCAN
  • Text Analytics
  • Tokenizing, Chunking
  • Document term
  • Matrix TFIDF
  • Sentiment analysis hands-on
  • Naive Bayes classifier