Python Web Development and Machine Learning/Data Science Training

Python Web Development and Machine Learning Training

Duration: 50 hours

Weekdays and Weekend batches available

Data Science / Course Training – Masters Program

Duration: 70 hours

Week Days and Week End Batches

Convenient Time Schedules

Data Science Course / Training Master program Syllabus

Module 1: Introduction to Data Science (Duration-1hr)

  • What is Data Science?
  • What is Machine Learning?
  • What is Deep Learning?
  • What is AI?

Module 2: Introduction to Python (Duration-1hr)

  • What is Python?
  • Why Python?
  • Installing Python
  • Python IDEs
  • Jupyter Notebook Overview

Module 3: Python Basics (Duration-12 hrs)

  • Variable and Data types
  • Selection by position & Labels
  • IF statements
  • Dictionaries
  • Operators
  • Control structure
  • Looping controls
  • Functions
  • Lambda
  • Array
  • Scope
  • Modules
  • Object Oriented Programming
  • Regular expressions
  • File Handling

Hands-on-Exercise-Constructing Operators

Practice and Quickly learn Python necessary skills by solving simple questions and problems.

How Python uses indentation to structure a program, and how to avoid some common indentation errors.

Exercise on lists, tuple, dictionary and set

Module 4: Python Packages (Duration-2hrs)

  • Pandas
  • Numpy
  • Sci-kit Learn
  • Mat-plot library

Module 5: Importing Data (Duration-1hr)

  • Reading CSV files
  • Saving in Python data
  • Loading Python data objects
  • Writing data to CSV file

Hands-on-Exercise:

Create visualizations of that data. You learned to create simple plots with matplotlib, and you saw how to use a scatter plot.

Module 6: Manipulating Data (Duration-1hr)

  • Selecting rows/observations
  • Rounding Number
  • Selecting columns/fields
  • Merging data
  • Data aggregation
  • Data munging techniques

Hands-on-Exercise:

Handle CSV and JSON files and analyze.

Most online data sets can be downloaded in either or both of these formats.

Module 7: Statistics Basics (Duration-11hrs)

  • Central Tendency
  • Mean
  • Median
  • Mode
  • Skewness
  • Normal Distribution
  • Probability Basics
  • What does it mean by probability?
  • Types of Probability
  • ODDS Ratio?
  • Standard Deviation
  • Data deviation & distribution
  • Variance
  • Bias variance Tradeoff
  • Underfitting
  • Overfitting
  • Distance metrics
  • Euclidean Distance
  • Manhattan Distance
  • Outlier analysis
  • What is an Outlier?
  • Inter Quartile Range
  • Box & whisker plot
  • Upper Whisker
  • Lower Whisker
  • Scatter plot
  • Missing Value treatment
  • What is NA?
  • Central Imputation
  • KNN imputation
  • Dummification
  • Correlation
  • Pearson correlation
  • positive & Negative correlation

Hands-on-Exercise:

Handle outlier in a dataset

Handle null values on a dataset

Module 8: Machine Learning (15 hrs)

  • EDA & Preprocessing
  • Regression
  • Regularization
  • K-Nearest Neighbors
  • Logistic Regression
  • Naïve Bayes
  • Support Vector Machine
  • Decision Tree
  • Bagging & Boosting
  • Random Forest
  • K-Means Clustering
  • Hierarchical Clustering
  • Principle Component Analysis
  • Association Rule

Module 9: Error Metrics (4 hrs)

  • Confusion Matrix
  • Precision
  • Recall
  • Specificity
  • F1 Score
  • MSE
  • RMSE
  • MAE

Unsupervised Learning (Duration-4hrs)

Module 10: Deep learning  (3 hrs)

  • Perceptron
  • Forward and backward propagation
  • Gradient Descent
  • Activation function
  • Dropout
  • DL  Applications

Module 11: Deep Learning Algorithms (Duration-10hrs)

  • CNN – Convolutional Neural Network
  • RNN – Recurrent Neural Network
  • ANN – Artificial Neural Network

Hands-on-Exercise:

Implement a neural network.

Write novel architectures.

Module 12: Introduction to NLP (Duration-5hrs)

  • Text Pre-processing
  • Noise Removal
  • Lexicon Normalization
  • Lemmatization
  • Stemming
  • Object Standardization

Module 13: Text to Features (Feature Engineering) (Duration-5hrs)

  • Syntactical Parsing
  • Dependency Grammar
  • Part of Speech Tagging
  • Entity Parsing
  • Named Entity Recognition
  • N-Grams
  • TF – IDF
  • Frequency / Density Features
  • Word Embedding’s

Tasks of NLP (Duration-2hrs)

  • Text Classification
  • Text Matching

Project Work

Project 1: Classification model

Liver Disease classification based on ML model

Project 2: Classification model

Credit card Fraud analysis

Project 3: Regression model

Real estate price prediction

Stock price prediction

Project 4: Clustering model

Customer segmentation

Project 5: Image classification

Handwritten character recognition

Project 6: Text classification

Sentiment analysis on social media data

Project 7:  Movie recommendation

Recommendation system

Contact Us Now and Get Enroll on this Training Program:

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