DATA SCIENCE | Demo Vedio| Outline|Duration: 45 Hours |Class Room & Online Training

Data Science – with Python Course Outline

Welcome to the course!

  • Applications of Machine Learning
  • Why Machine Learning is the Future
  • Installing Python and Anaconda (MAC & Windows)
  • Python for data Science:

  • A Good First Program
  • Comments and Pound Characters
  • Numbers and Math
  • Variables and Names
  • More Variables and Printing
  • Strings and Text
  • Prompting People,
  • Parameters, Unpacking, Variables,
  • Prompting and Passing Reading Files,
  • Reading and Writing Files
  • Names, Variables, Code, Functions
  • Functions and Variables
  • Functions and Files
  • Boolean Practice
  • What If
  • Else and If - Making Decisions
  • Loops and Lists, While Loops, Accessing
  • Elements of Lists
  • Branches and Functions
  • Designing and Debugging
  • Dictionaries
  • Modules, Classes, and Objects
  • NumPy and Pandas Lib Exploration

  • Welcome to the NumPy Section!
  • Introduction to Numpy
  • Numpy Arrays
  • Quick Note on Array Indexing
  • Numpy Array Indexing
  • Numpy Operations
  • Introduction to Pandas
  • Series
  • DataFrames
  • Missing Data
  • Groupby
  • Meraging Joining and Concatinating
  • Operations
  • Data Input Output
  • Data Visualization using Python Matplotlib and Tableau

  • Maplotlib
  • Seaborn
  • Tableau / Qlikview
  • Excel
  • Data Preprocessing for Algorithm

  • Get the dataset
  • Importing the Libraries
  • Importing the Dataset
  • Object-oriented programming: classes , objects , Lib
  • Missing Data
  • Categorical Data
  • Splitting the Dataset into the Training set and Test set
  • Feature Scaling
  • Data Preprocessing Template!
  • Regression

  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Support Vector Regression (SVR)
  • Decision Tree Regression
  • Random Forest Regression
  • Evaluating Regression Models Performance
  • i. R-Squared Intuition
  • ii.Adjusted R-Squared Intuition
  • iii.Evaluating Regression Models Performance
  • iv.Interpreting Linear Regression Coefficients
  • Classification

  • Logistic Regression
  • K-Nearest Neighbors (K-NN)
  • Support Vector Machine (SVM)
  • Kernel SVM
  • Naive Bayes
  • Random Forest Classification
  • Evaluating Classification Models Performance
  • i.False Positives & False Negatives
  • ii.Confusion Matrix
  • iii.Accuracy Paradox
  • iv.CAP Curve
  • Clustering

  • K-Means Clustering
  • Hierarchical Clustering
  • Association Rule Learning

  • Apriori
  • Artificial Intelligence (Reinforcement Learning)

  • Upper Confidence Bound (UCB)
  • Thompson Sampling
  • Natural Language Processing

  • Text Classification
  • Sentiment Analysis
  • Deep Learning

  • Artificial Neural Networks
  • Convolutional Neural Networks
  • Dimensionality Reduction

  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Kernel PCA
  • Model Selection & Boosting

  • K-Fold Cross Validation
  • Grid Search
  • XGBoost
  • Machine Learning Model Deployement

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