IIRF Online > Development > Data Science > Machine Learning > Master Data Science & Machine Learning in Python
Master Data Science & Machine Learning in Python by Great Learning
The Python for Data Science and Machine Learning course equips learners with skills in data analysis, predictive modeling and feature engineering. Build intelligent machine learning solutions to tackle real-world challenges.
Skills you will learn!
Curriculum
8 Topics
Introduction to Numpy
Indexing an Array
Slicing an Array
Operations on an Array
Arithmetic Functioning in Numpy
Concatenation of Array
Coding Exercise on Numpy - Beginner
Coding Exercise on Numpy - Intermediate
16 Topics
Introduction to Pandas
Introduction to Data Structures
Introduction to Pandas Series and Creating Series
Manipulating Series
Introduction to Dataframes and Creating Dataframe
Manipulating the Dataframes
Reading Data From Different Sources
Concatenate
Merging and Joining a Dataframe
Re-shaping the Dataframe
Pivot Table
Duplicate
Map and Reduce
Group-by in Pandas
Coding Exercise on Pandas - Intermediate
Coding Exercise on Pandas - Intermediate
16 Topics
Introduction to Visualization Libraries
Line Plot
Scatter Plot
Bar Plot
Pie Plot
Histogram Plot
Box Plot
Strip Plot
Swarm Plot
Violin Plot
Pair Plot
Distribution Plot
Heat Map
Count Plot
Coding Exercise on Visualization - Beginner
Coding Exercise on Visualization - Advance
14 Topics
Introduction to EDA
Descriptive Data Measures
5-Point Summary and Skewness of Data
Box-Plot Covariance and Coeff of Correlation
Let's Get Our Hands Dirty with Code
Univariate and Multivariate Analysis
Encoding Categorical Data
Scaling and Normalization
What is Preprocessing?
Imputing Missing Values
Working with Outliers
Case Study Analysis
Coding Exercise on EDA - Beginner
Coding Exercise on EDA - Intermediate
9 Topics
Introduction to Machine Learning
Steps of Machine Learning
Introduction to Scikit Learn
What is Scikit learn?
Installing Scikit learn
Support for Algorithms
Applications of Scikit learn
Advantages and Disadvantages
Practical Demo in Python
13 Topics
Supervised machine learning - Introduction
Linear regression and its Pearson’s coefficient
Linear regression mathematically and coefficient of Determinant
Brief Scenario of Dataset and Descriptive analysis
Analyse the Distribution - Dependent column
Missing Values Imputation
Bivariate analysis using plots through Seaborn function
Building model using all Information
Exploratory Data Analysis (EDA)
Model Analysis and Squared errors
Summary and Lab exercise of linear regression
Coding Exercise on Linear Regression - Beginner
Coding Exercise on Linear Regression - Advance
5 Topics
Classification Algorithm: Logistic Regression
Logistic Regression Model and Sigmoid Function
Logistic Regression: Confusion Matrix Precision and Recall (Hands-on)
Beginner-Level Coding Exercise on Logistic Regression
Advanced-Level Coding Exercise on Logistic Regression
5 Topics
Bayes Theorem
Introduction to Naive Bayes Classifier
Introduction to Naive Bayes Classifier and Examples
Naive Bayes - Hands-on
Naive Bayes Coding Exercise
8 Topics
Decision Trees introduction
Decision Trees CART Algorithm
Loss Function- Entropy
Loss Function - Gini
Decision Trees - Conclusion
Decision Trees - Hands-on
Decision Trees Coding exercises - Beginner
Decision Trees Coding exercises - Advanced
14 Topics
Ensemble Methods
Bagging
Bagging - Hands on
Boosting
Types of Boosting
Adaboosting - Hands on exercise
Gradient Boosting - Lab exercise
Random Forest
Random Forest - Hands on exercise
Coding Exercise Bagging
Coding Exercise Adaboosting - Beginner
Coding Exercise Adaboosting - Advance
Coding Exercise Gradient Boosting - Advanced
Coding Exercise Random Forest - Beginner
7 Topics
Unsupervised Learning
Clustering - Types and Distance
Clustering - Distance Calculations
K-Means Clustering
Elbow Method
Coding Exercise on K-Means Clustering - Beginner
Coding Exercise on K-Means Clustering - Intermediate
10 Topics
Introduction to Feature Engineering
Hands on exercise - Feature engineering
Cross validation concept and procedure
Implementing K Fold Cross Validation
Some salient features of K-fold
Bootstrap Sampling Concept and Hands-on
Leave one out Cross Validation (LOOCV) Concept
Hands-on Implementation of LOOCV Technique
Up sampling and down sampling
Coding Exercise on Feature Engineering and Cross Validation - Beginner
6 Topics
Model Tuning and Performance
Hyper parameters and Tuning
GridSearch
RandomizedSearch CV
Hands on exercise on RandomizedSearch CV and GridSearch CV
Coding Exercise on Grid Search - Beginner
Master Data Science & Machine Learning in Python
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