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Python for data science refers to the extensive libraries and tools the programming language offers for data and numerical analysis, as well as its capacity for machine learning tools that can improve analytics in general. In recent years Python has become an increasingly popular programming language due to its versatility and multi-purpose design.

As opposed to domain-specific languages and those that are designed for unique purposes, Python provides a range of tools that make it valuable not just as a data science platform, but also as a foundation to build more expansive applications that include data science tools.

Course Content

  • Introduction to Data Science
  • Introduction to NumPy and Matplotlib
  • Matrix Operations with NumPy
  • Random Variable and Probability Distributions
  • Probability
  • Properties of Probability Distributions
  • Mean, Median, Mode
  • Variance, Skewness, Kurtosis
  • Multivariate Normal Distribution
  • Co-Variance, Correlation
  • Introduction to Scikit-Learn
  • Data Pre-Processing Techniques using Scikit-Learn
  • Dimensionality Reduction as Data Pre-Processing
  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Using Python for analysis Univariate Data
  • Important Python Libraries
  • Tables, Histogram, Boxplot in Python
  • Case Study Tables, Histogram, Boxplot using NHANES dataset
  • Introduction to Reinforcement Learning
  • Q-Learning with Python
  • Introduction to Clustering
  • K-Means Clustering
  • Agglomerative Clustering
  • Introduction to Supervised Learning
  • Naive Bayes Classification
  • Linear and Polynomial Regression
  • K-Nearest Neighbors
  • Decision Tree
  • Balancing Bias vs Variance of ML Model
  • Ensemble Learning
  • Random Forest and Adaptive Boost
  • Identifying Important Features of Data
  • Introduction to Logistic Regression
  • Computation Graph and Gradient Descent
  • Introduction to Artificial Neuron (Perceptron)
  • Multi-Layer Perceptron
  • Introduction to Artificial Neural Networks
  • Designing Artificial Neural Networks with Keras
  • Gradient Descent Variants
  • Classification and Regression using Neural Networks
  • Introduction to Convolutional Neural Network (CNN)
  • Object Classification with CNN
  • Standard CNN Architectures
  • Introduction to Object Detection
  • The YOLO Algorithm
  • Introduction to NLTK
  • Text Pre-Processing
  • POS Tagging and Named-Entity Recognition
  • Latent Semantic Analysis
  • Introduction to Recurrent Neural Network
  • Word2Vec Algorithm for Text Vectorization
  • Natural Language Processing with LSTM
  • Giving Web Interface to ML Application using Flask/Django

About the instructor


BI Solution Architect

Hi I am Dipu Maharjan. I am working as BI Solution Architect. I have experience more than 13 years as Data Analyst, Database Programmer, BI Developer and Trainer.

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    Course Features

  • Prerequisite:Core Python
  • Total Credit Hours60 hrs
  • Course CostUSD 224.70