Data Science Course (2)

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• Supervised Learning Predictive Modeling with
  Labeled Data
• Understanding Supervised Learning
• Start by explaining the concept of supervised
  learning, which involves training a model on a
  labeled dataset consisting of input features and
  corresponding target labels. Data Science
  Course. Emphasize that the goal of supervised
  learning is to learn a mapping from input
  features to output labels based on the labeled
  examples provided during training.
• Types of Supervised Learning Algorithms
• Introduce the main types of supervised learning
  algorithms classification and regression.
  Explain that classification algorithms are used
  for predicting discrete class labels, while
  regression algorithms are used for predicting
  continuous numerical values. Provide examples of
  common algorithms in each category, such as
  logistic regression, decision trees, random
  forests, support vector machines (SVM), and
  neural networks.
• Data Preprocessing and Feature Engineering
• Discuss the importance of data preprocessing and
  feature engineering in supervised learning.
  Teach students to clean and preprocess the
  dataset by handling missing values, encoding
  categorical variables, and scaling numerical
  features. Explain how feature engineering
  techniques such as feature selection,
  dimensionality reduction, and creating new
  features can improve model performance and
  generalization.
• Model Training and Evaluation
• Cover the process of model training and
  evaluation in supervised learning. Explain how to
  split the dataset into training and testing sets
  to assess the model's performance on unseen
  data. Introduce evaluation metrics appropriate
  for classification tasks (e.g., accuracy,
  precision, recall, F1-score, ROC AUC) and
  regression tasks (e.g., mean absolute error, mean
  squared error, R-squared). Teach students how to
  select the appropriate evaluation metric based on
  the specific problem and interpret the model's
  performance results.
• Model Selection and Hyperparameter Tuning

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exploring the hyperparameter space and selecting
the optimal combination of hyperparameters.
Emphasize the need for experimentation and
iteration to fine-tune the model and achieve the
best performance. By mastering these pointers,
students can effectively apply supervised
learning techniques to build predictive models
using labelled data. Data Science Course in
Mumbai. They will gain a solid understanding of
the fundamental concepts, algorithms, and best
practices in supervised learning, enabling them
to tackle a wide range of classification and
regression tasks in real-world
applications. Business name ExcelR- Data
Science, Data Analytics, Business Analytics
Course Training Mumbai Address 304, 3rd Floor,
Pratibha Building. Three Petrol pump, Lal Bahadur
Shastri Rd, opposite Manas Tower, Pakhdi, Thane
West, Thane, Maharashtra 400602 Phone
09108238354, Email enquiry_at_excelr.com