Curriculum
- 13 Sections
- 0 Lessons
- 45 Hours
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- Module 1: Introduction to Machine Learning and Python
- What is Machine learning
- Types of machine learning
- Supervised, unsupervised
- Use of python is this domain
- Feature of python
- Software installation
- If else loops in python
- Function and module
- Class, object in python
- String manipulation
- Data structures in python(list, tuple, dictionary)
- List and dictionary comprehension
0 - Module 2: ML 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
0 - Module 3: 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
0 - Module 4: Supervised Machine learning – Regression
- Explain supervised machine learning
- Difference between classification and regression
- Concepts of train data and test data
- K fold cross validation vs train test split
- Types of regression problem, linear regression , polynomial regression
- Simple Linear Regression and it uses
- Apply polynomial regression for non linear dataset
- What is r2score and RMSE score
0 - Module 5: Gradient Descent and Multivariate Regression
- Multiple linear regression
- Condition for multivariate linear regression
- Gradient descend algorithm
- How gradient descend works
- Use gradient descend to optimize linear regression parameter
0 - 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
- Handling imbalanced classification problem
- approaches to handle imbalanced data
0 - Module 7: Tree Based Algorithm
- concept of tree based algorithm
- decision tree algorithm
- maths behind decision tree
- standard deviation reduction for regression
- entropy and gini index for classification problem
- pruning of tree
- overfiting in decision and its solution
0 - Module 8: Ensemble learning and Boosting
- concepts of ensemble learning
- what is bagging and boosting
- random forest for bagging
- advantage and disadvantage of random forest
- random forest for both regression and classification
- adaboost and gradient boost
- use case for both boosting technique
0 - Module 9: Naive Bayes algorithm for text classification
- what is naïve bayes
- bayes theorem and conditional probability
- types of naïve bayes
- Countvectorizer and tfidfvectorizer for text
0 - Module 10: SVM and Kernel trick
- Support vector machine and its uses
- Concepts of decision boundary, linear SVM
- SVM for non linear problem
- Kernel trick, poly, linear, rdf Affect of gamma and C in SVM
0 - Module 11: Unsupervised learning and Dimensionality Reduction
- What is unsupervised learning
- Clustering problem
- K means clustering
- Concept of dimensionality reduction
- Feature extraction and feature elimination
- PCA and its uses
0 - Module 12: Natural Language Processing
- Lexical Processing
- Syntactic Processing
- Semantic Processing
0 - Module 13: Project Work & Documentation0
