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Course Curriculum

PRE-REQUISITE
Student should have knowledge and some experience in any programming language. 00:00:00
In case if student isn’t well versed with any programming language then please talk to counsellor before enrolling to this program 00:00:00
1. DATA ANALYSIS AND PRE-PROCESSING
Module 1.1: Python for Data Analysis (6 Hours)
The objective of this module is to understand various syntax of Python programming language and its use in data analysis. 00:00:00
Topics covered in this module are-
Anaconda Spyder/ Jupyter Notebook 00:00:00
Running Python programs 00:00:00
Variable. Keyword, operator 00:00:00
Data Structures-List, Tuple, Set, Dictionary 00:00:00
Control flow 00:00:00
Loops 00:00:00
Local variable 00:00:00
Function 00:00:00
Module 1.2: Data Visualization and Analysis (06 Hours)
The objective of this module is to
understand data through visual charts 00:00:00
analyse data and detect anomaly in data 00:00:00
prepare and pre-process data for machine learning 00:00:00
Topics covered in this module are -
Learn libraries - Seaborn and Matplotlib
Visualize various plots
Histogram 00:00:00
Bar chart 00:00:00
Scatter plot 00:00:00
Pie chart 00:00:00
Heat plot 00:00:00
Learn libraries - Pandas and NumPy
Pandas
DataFrame 00:00:00
Data View 00:00:00
Data Selection 00:00:00
Indexing Merge 00:00:00
Append 00:00:00
Grouping 00:00:00
Reshaping 00:00:00
Categorical 00:00:00
Data in and out 00:00:00
Numpy
Array Creation 00:00:00
Array Operations 00:00:00
Array Indexing, Slicing and Iterating 00:00:00
Array Splitting 00:00:00
Broadcasting 00:00:00
Date and Time 00:00:00
Descriptive Statistics
Correlation
Missing Value Imputation
Data Normalization and Standardization
2. MACHINE LEARNING
Machine learning will be taught based on applications, theory and mathematics/logic behind each algorithm. Each algorithm will be explained by implementing use case using Python. It will also cover situations, suitability and comparison of algorithms. 00:00:00
Module 2.1: Machine Learning - Regression (12 Hours)
The objective of this module is to cover regression family of algorithms used in machine learning. 00:00:00
Topics covered in this module are-
Ordinal Least Square Regression 00:00:00
Lasso Regression 00:00:00
Polynomial Regression 00:00:00
Forward Feature Regression 00:00:00
Step Wise Regression 00:00:00
Regression Model Fine Tuning 00:00:00
Module 2.2: Machine Learning - Classification and Clustering (36 Hours)
The objective of this module is to cover classification and clustering family of algorithms used in machine learning. 00:00:00
Topics covered in this module are-
Classification Algorithms
Logistic Regression 00:00:00
Naive Bayes 00:00:00
Decision Tree 00:00:00
Support Vector Machines 00:00:00
K-Nearest Neighbour 00:00:00
Confusion Matrix
Classification Model Fine Tuning
Ensemble Learning
Random Forest 00:00:00
AdaBoost 00:00:00
Gradient Boosting 00:00:00
XGBoost 00:00:00
Clustering Algorithms
Hierarchical Clustering 00:00:00
K-means Clustering 00:00:00
DBSCAN 00:00:00
Projects with real life examples
Predicting the price of house 00:00:00
Exploring the Titanic data set-Survived vs Not-Survived 00:00:00
Identifying SPAM vs Non-SPAM in emails 00:00:00
Twitter sentiment Analysis 00:00:00
Machine failure prediction 00:00:00
Salary of CEO prediction 00:00:00
Diabetic prediction from human data 00:00:00
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