Python for Machine Learning

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Course Description

Data Science, Machine Learning, Deep Learning and Artificial Intelligence

Objectives of the Program

  •  To enable participants understand how Data Science is used in every aspect of our daily lives and businesses
  •  To enable the participants to learn basics of Python.
  •  To enable the participants to learn Data Science/Machine learning/Deep Learning using Python.
  •  To empower the participants with sufficient knowledge to use latest technologies of Data Science (Hands-On).
  •  To make the participants understand overview of how Exploratory Data Analysis works.
  •  To provide the participants Hands-on experience in Data science concepts.
  •  To provide the participants Hands-on experience in Machine Learning concepts.
  •  To empower the participants with sufficient knowledge to use latest technologies of Data Science/Machine Learning/Deep Learning/ Artificial Intelligence (Hands-On).
  •  Overview of the Program Content
  •  Introduction to Data Science, Machine Learning, Deep Learning and Artificial Intelligence
  •  Basics of Python and Hands-on on Python
  •  Introduction to Data Science using Python
  •  Hands on session on Data Science with Python [Database Connectivity with PYTHON: Performing Database Transactions (Inserting, Deleting, and Updating the   Database)]
  •  Introduction to Machine Learning and its types
  •  Examples Overview of Machine Learning packages in Python
  •  Introduction to Deep Learning and its types
  •  Project in Data Science/Machine Learning /Artificial Intelligence


Module 1: Python for Data Science/Machine Learning/Deep Learning

  •  Basic building blocks of python
  •  Data Types
  •  Operators
  •  Control Structure
  •  Functions
  •  Data Structures
  •  String Operations and Regular Expressions
  •  File Handling
  •  Exception Handling
  •  The Object-Oriented Approach

Module 2: Maths Behind Data Science

  •  Statistical Analysis: Descriptive Statistics & Inferential Statistics
  •  Hypothesis Testing

Module 3: Data Science (Exploratory data analysis (EDA) /Data wrangling/
Data Munging/Data Cleaning Techniques/ Data Preparation/Feature

o Data Science Library and data visualization Using Python
o Data Exploration (Univariate/Bivariate/Multivariate Analysis)
o Missing value treatment
o Outlier treatment
o Categorical to Numerical
o Feature Rescaling
o Feature (Variable) transformations
o Correlation Analysis
o Feature selection Methods

Module 4: Data Visualization using Tableau

Module 5: Introduction to Machine Learning: Supervised Learning

  • Regression Analysis- Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Advanced Regression
  • Classification Methods- Logistic Regression/Naïve Bayes/KNN/ Support Vector Machines (SVM)/Decision Tree/Random Forest

Module 6: Supervised Learning: Classification Methods: Ensemble


  •  Bootstrap Aggregation (Bagging) Ensemble Learning
  •  Boosting Ensemble Learning

Module 7: Unsupervised Machine Learning

  •  Clustering Techniques
  •  Dimensionality Reductionality

Module 8: Model Tuning, Model Selection and Hyperparameters

  •  Cross Validation Techniques
  •  Model selection and Tuning
  •  Model Performance and Measure (Evaluation Metrics)
  •  Model Regularization Method
  •  Imbalance data Treatment
  •  Model Hyperparameter optimization
  •  ML Pipeline

Module 9: Introduction to Neural network for Deep Learning

  •  Introduction to Tensorflow/Keras/Pytorch
  •  Biological Neuron/ Artificial Neuron (Perceptron)
  •  Perceptron/MLP/ Vanilla Neural Network/ANN
  •  Types of ANN: Sequential ANN and Functional ANN
  •  Weight Initialization and Biases
  •  Activation Functions
  •  Backward Propogation
  •  Gradient Descent
  •  Learning rate
  •  Optimizers
  •  Regularization
  •  Loss Function and Cost Function
  •  Hyperparameters

Module 10: Computer vision

  •  Introduction to computer Vision and Image Processing
  •  Introduction to CNN
  •  Introduction to CNN Architectures and Transfer Learning
  •  Image Classification using CNN and Transfer Learning

Module 11: Advanced Computer Vision

  •  Object Detection
  •  Image Segmentation
  •  Semantic Segmentation

Module 12: Natural Language Processing (NLP) Basics

  •  Introduction to NLP
  •  Text Preprocessing
  •  Bag of words Model and TF-IDF
  •  Introduction to Language Model: N-grams/Word2vec/Glove

Module 13: Sequential Models and NLP

  •  Introduction Recurrent Neural Network (RNN)
  •  Introduction to Long Short term Memory (LSTM)
  •  Introduction to GRU
  •  Introduction to Encoder –Decoder
  •  Introduction to Transformer
  •  Introduction to BERT
  •  Introduction to GPT3

Module 14: Unsupervised Deep Learning
o Introduction to GAN and its types
o Introduction to Autoencoder

List of Projects for Internship/Final Year Projects
1. EDA in Covid-19 Live Tracking Analysis from Government Website
2. Cancer Prediction using Machine Learning
3. Machine Learning for Classification of Fraudulent and Non-Fraudulent Transactions
4. Credit Card Fraud Detection Project
5. Customer Segmentation
6. Customer Churn Prediction
7. Recommendation System
8. Housing Prices Prediction Project
9. Movies Award Prediction
10. MNIST Handwritten Digit Image Classification using Artificial Neural Network (ANN)
11. MNIST Handwritten Digit Image Classification using Convolution Neural Network (CNN)
12. Bean Leaf Disease Image Classification
13. Human vs Horse Image Classification
14. Cat Vs Dog Image Classification
15. Chatbots
16. Sentiment Analysis
17. Image to Image Generation
18. Denoising of Images using Autoencoder
19. GAN
20. Image super resolution using GAN

Tools, Languages and Libraries Used:

1. Sql
2. Python
3. Anaconda
4. Jupyter Notebook
5. Google Colab
6. Tableau
7. Numpy
8. Pandas
9. Matplotlib

10. Seaborn
11. Sklearn
12. Scipy
13. Tensorflow
14. Keras