Objective: On completing this course, there will be ample opportunities for you to work as a Machine learning engineer or Data Scientist. You will be able to do some real world concepts like Data wrangling, feature engineering, supervised learning etc. After completion of this course will be able work on real life projects like House Price prediction, stock market analysis etc. You can also connect your machine learning model with some web based dashboard.
Technical Prerequisite: Knowledge of Python will be required.
Module 1: Introduction to Machine Learning
- What is Machine learning
- The three different types of machine learning
- Supervised, unsupervised, reinforcement learning
- An introduction to the basic terminology and notations
- A roadmap for building machine learning systems
- Different languages used for machine learning
- Uses of Machine learning in real life example
- Software used in machine learning and installation of software
Module 2: Revisited to Python
- Introduction to Python language
- Data types of Python, numbers, string
- If, elif, Loops in python
- Functions and modules in python
- Lambda function
- Create class and object in python
- Creating and accessing strings
- Indexing and slicing on string
- Strings methods
- List and its methods
- Accessing lists
- Tuple, set, dictionary and their methods
- List comprehension and its uses
Module 3:Machine learning libraries of Python
- Understanding the uses of various open source libraries
- Importing various modules with different methods
- Working with Numpy
- Numerical operations on numpy array
- Exploring various use cases of numpy
- Fundamental of Pandas
- Series and DataFrame
- Different functions on dataframe
- Pandas plotting functions
- Read external dataset using Pandas
Module 4: Data Pre-processing and Visualization
- Need of pre-processing of data
- What is Data Wrangling and feature engineering
- Introduction to sklearn module of python
- Handling different pre processing technique like missing value impute, explore data, convert from string to number etc
- Concepts of normalization and standardisation
- Standardize the dataset using StandardScalar(), MaxMinScalar()
- Fundamental of Matplotlib and Seaborn
- Various 2D and 3D graphs
- Data visualization in different types of graphs
Module 5: Supervised Machine learning – Regression
- Explain supervised machine learning
- Difference between classification and regression
- Concepts of train data and test data
- Types of regression problem, linear regression , polynomial regression
- Simple Linear Regression and it uses
- Multiple linear regression
- What is r2score and RMSE score
Module 6: Supervised Machine learning – Classification
- Different types of classifier
- LogisticRegression to solve classification problem
- Check for accuracy metrics for classification
- Confusion matrix, classification report
- Understanding the mathematics and working of KNN
- Implement KNN algorithm on your dataset
- Application of KNN
- Naïve bayes algorithm and its uses
Module 7: Tree based and ensemble learning
- Concept of tree based algorithm
- Pruning of tree
- Decision tree algorithm for both regression and classification
- Hyper parameter tuning in tree based algorithm
- Use of GridSearch in hyper parameter tuning
- Concept of cross validation, K fold CV
- What is ensemble learning
- Boosting and bagging in ensemble
- Gradient boost, XGBoost algorithm
- Random forest algorithm
Module 8: Unsupervised learning
- Unsupervised learning and its type
- Dimensionality reduction using PCA
- Clustering using K means
Module 9: Host machine learning model on web
- What is Flask, install flask
- Create folder structure in flask
- Embed flask into your application
- Set up routes
- Implement web pages using HTML
- Run and deploy the application
Projects:-
- Impact of Covid 19 on Global economy
- Employee salary prediction
- Crime data analysis and prediction
- Earthquake prediction
- Customer segmentation
- Heart disease prediction
