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
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