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About
Data Science (Machine Learning)
This course teaches how to use Python for Data Science and Machine Learning. It takes you through the life cycle of Data Science project using tools and libraries in Python.
Prerequisite
Python Language
Theory Fee
Rs. 6500/- (Includes digital course material)
Digital or Physical Certificate Fee
Rs. 200/-
Software Required
Anaconda from
anaconda.com/downloads
Detailed Syllabus
Introduction to Data Science
What is Data Science
What is Machine Learning
What is Deep Learning
Role of Data Scientist
Applications of Data Science
Data and its sources
Overview of Data Science Life Cycle
Working with Anaconda and Jupyter Notebook
Downloading and installing Anaconda
Starting Jupyter Notebook
UI elements of Notebook
Kernel and types of cells - Code and Markdown
Modes - Edit and Command
Magic functions - Line and Cell functions
Keyboard shortcuts - Command mode and Edit mode shortcuts
Saving and loading of notebook
Using Jupyter Lab
Basic Statistics
Mean, Median, Mode and Range
Using statistics module
Variance and Standard Deviation
Quartiles and IQR
Understanding distribution of data using Histogram and Box plot
Measuring Skewness and Kurtosis
Probability
Correlation between variables
Using scipy.stats module
Scatter plot to understand correlation
Regression Analysis
Understanding intercept and slope - predict y given X
Numpy
Creating single and multi-dimensional arrays
Using indexing and slicing
Using fancy indexing (boolean indexing and array of indices)
Array operations, methods of ndarray and universal functions
View vs. Copy of array
Reshaping arrays
Stacking and splitting arrays
Broadcasting
Applying Linear Algebra
Image processing with Arrays
Pandas
Working with Series
Applying methods on Series
Working with DataFrame
Reading data into DataFrame and writing DataFrame to other formats
Selecting rows and columns in DataFrame
Adding and deleting rows and columns in DataFrame
Working with apply() and applymap() functions
Working with str attribute for string manipulations
Joining, Merging and Concatenating DataFrames
Grouping data on one or more columns
Using pivot_table()
Data Wrangling - Binning, Encoding etc.
Handling null values
Drawing plots using Pandas
Matplotlib
Anatomy of a figure
Working with Module API and Object API
Working with different plots - Histogram, Bar, Stacked Bar, Pie, Scatter, Line
Creating multiple axes in single figure
Customizing plots - labels, legends, scales, titles, text etc.
Seaborn
Figure-level vs. Axes level plots
Categorical, Relational, Distribution, Regression and Matrix Plots
Using parameters like hue, row and col
Data Science Workflow (Life Cycle) using Classification Case Study
What is the question
Data Acquisition
Preparing data - cleaning and organizing data
Exploratory Data Analysis (EDA)
Data Munging/Data Wrangling
Feature Engineering
Data Visualization
Model Building
Model Evaluation
Model Deployment
Machine Learning Workflow with Classification Case Study
Understanding pre-processing concepts like Standardization, Encoding etc.
Training Model using train and test split
Using different algorithms like Logistic Regression, Support Vector Machines, k-Nearest Neighbors, Naive Bayes, Decision Tree, Random Forest using Scikit-learn
Evaluating result of the model using metrics - classification report, confusion matrix
Understand Precision, Recall, F1 Score, Specificity and Sensitivity
Understanding cross validation and how to use it to train and test model
Presenting the model - Deployment of the model
Working with Regression case study
How to use metrics - MSE, RMSE, R2 Score etc. to evaluate model
Understanding Regularization - Lasso and Ridge
Understanding ensemble algorithms - Bagging and Boosting
Stochastic Gradient Descent
Using Grid Search to select right hyper parameters
How to use Pipeline
Understanding non-linear data - polynomial features
Unsupervised Machine Learning - Clustering and Association
What is clustering
How k-Means clustering works
How DBSCAN works to create clusters
How hierarchical clustering works - Agglomerative clustering and Dendrogram
Recommender systems - Collaborative filtering and Content-based filtering