NodeBook Private Limited

Machine Learning (Artificial Intelligence) – Level3

Applied Data Science with Machine Learning

  • Bring your own laptop.
  • Knowledge of Level 2 is required.

Note: Training is completely practical on real world industrial datasets to achieve mastery for an international market.

In this training, you’ll be hands-on on how to master mandatory data scientist technical skills, including
object-oriented and functional approaches with Python, and libraries like scikit-learn, Matplotlib,
NumPy, and pandas. You’ll also master web scraping and database queries, deep learning and machine
learning, and predictive analysis.

To help you stand out from others, we included concepts such as Git, and GitHub to develop efficient
collaboration. Best of all, you’ll learn by doing and apply your skills to several projects involving
realistic business scenarios to build your portfolio and prepare for the international market.

  • Foreign and Local Experienced Trainers;
  • Hands-on Training with Real Working Environment;
  • Internship Opportunities; Affordable Cost;
  • Continuous help to the Participants even after Training sessions via whatsapp groups;
  • Guided projects, Profile building and Specific Resume designing are also done usually during gap between two levels.

As Data Science & Data Literacy are going to be mandatory and regulatory requirements for all domains and industries, any professional belonging to Finance, HR & Admin, Audit, Supply Chain, Engineering, Computer Science, Health Care, etc. are eligible for this training.

Knowledge of LEVEL-2 is required.

Nearest Neighbors:

    • Euclidean Distance
    • Randomizing and Sorting
    • Function to Make Predictions
    • Testing quality of predictions
    • Error Metrics
    • Mean Squared Error
    • Root Mean Squared Error
    • Comparison of Errors
    • Removing features
    • Handling missing values
    • Normalization
    • Euclidean distance for multivariate case
    • Fitting a model and making predictions
    • Hyperparameter optimization
    • Grid Search
    • Visualization of Hyperparameter Values
    • Varying Hyperparameters
    • Holdout Validation
    • K-Fold Cross Validation
    • Bias-Variance Tradeoff
    • Univariate Model
    • Multivariate Model

Linear Regression:

    • Instance Based Learning Vs. Model Based Learning
    • Simple Linear Regression
    • Least Squares
    • Multiple Linear Regression
    • Correlating Feature
    • Correlation Matrix Heatmap
    • Train And Test Model
    • Low Variance Features
    • Single Variable Gradient Descent
    • Derivative
    • Multi Parameter Gradient Descent
    • Cost Function
    • Gradient Of The Cost Function
    • Gradient Descent For Higher Dimensions
    • Gradient Descent vs. Ordinary Least Squares
    • Categorical Features
    • Dummy Coding
    • Transformation of Features
    • Imputing Missing Values
    • Feature Engineering
    • Feature Selection

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