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Last Date to Apply
January 26, 2020
Program Starts
January 29, 2021
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Machine Learning With Python
Instructor
Will be announced soon...
Course Description
Introduction:
- Introduction to Data Science and Machine Learning
- Examples & Techniques of Machine Learning
- Real Life Industry Based ML Problem Statements
Python:
- Python Installation Guidelines
- How to use Jupyter Notebook
- Introduction to Python
- Python programs
- Python Data Structures-List
- Python Data Structures-Dictionary
- Python Data Structures-Sets
- Python Data Structures-Strings
- Python Data Structures-Tuples
- Python Numpy
- Python Pandas-Data Frames
- Python Pandas-Series
- Python Pandas-Quick-tips
Statistics:
- Basics of Statistics
- Central Tendency
- Covariance
- Correlation
- Standard Deviation
- Z-Score
Data Processing:
- Data processing techniques in Python
- Matplotlib Python visualization library
- Box Plots
- Histograms
- Label Encoding
- One Hot Encoding
- Training and Testing data
K-means
- Introduction to Clustering
- Understanding Income Group data set
- Understanding of K-means Algorithm
- Implementation of K-means in Python(Income Group)
- Elbow Test Method
Dimensionality Reduction:
- Principal Component Analysis(PCA)
- PCA implementation in Python
Statistics: Feature Scaling:
- Normalization
- Standardization
- Implementation of Feature scaling in Python
Decision Tree:
- Understanding of Decision Tree
- Identification of Root Node
- Implementation in Python
Evaluation: Confusion Matrix:
- Accuracy
- Recall
- Precision
- F-Score
Random Forest:
- Understanding of Random Forest
- Bagging Techniques
- Implementation in Python
K-Nearest Neighbours:
- Understanding of KNN algorithm
- Understanding of iris dataset
- Implementation in Python
Linear Regression:
- Understanding of Linear Regression
- Assumptions of Linear Regression
- Implementation in Python
- Problems in achieving accuracy of model
- R-Square
- Adjusted R-Square
- Bias
- Variance
- Trade-Off between Bias and Variance
Polynomial Regression:
- Understanding of Polynomial Regression
- Implementation in Python
- Visualization of output by changing parameters
Regularization Techniques
- Ridge Regression
- LASSO Regression
- Elastic Net Regression
Logistic Regression:
- Understanding of Logistic Regression
- Implementation in Python
- Pros and Cons
Naive Bayes:
- Understanding of Naives Bayes
- Examples
- Pros and Cons
- Implementation in Python
Support Vector Machines:
- Understanding of Support Vector Machines(SVM)
- Understanding of different Scenarios
- Pros and Cons
- Implementation in Python
Azure Cloud: Azure Machine Learning Studio:
- Understanding of Azure ML Studio
- Implementation of case study in Azure ML Studio Demo
NLP Basics:
- What are NLP and NLTK?
- NLP setup and overview
- Reading the text data
- Exploring the dataset
- What are Regular Expressions
- Machine Learning Pipeline
Implementation:
- Removing Punctuation
- Tokenization
- Removing stop words
Supplementing Data Cleaning:
- Stemming
- Lemmatizing
- Text Summarizing-Word Cloud and Topic modelling
Vectorizing Raw Data:
- Count Vectorizing
- N-gram Vectorizing
- Inverse document frequency weighting
Sentimental Analysis in Jupyter Notebook
Real-Time Industry Applicable Projects
These projects can be used as your Mini or Mojor Projects
Image Recognition
Develop a software to identify objects, people, places, and actions in images.
Spam Detection
Develop a Spam Filter from Scratch Using Machine Learning.
Medical Diagnosis
Develop a medical test system to detect infections, conditions and diseases.
Social Media Sentiment Analysis
Develop a social media analytics tool that involves checking how many negative and positive keywords are present in a chunk of conversation.
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Frequently Asked Questions
- This Program is offered by India's best Industry expert with minimum Experience of > 4 years.
- It is an in-depth and comprehensive Program suitable for all aspirants.
- Unique 4-step learning process: Masterclass Lectures, Hands-on , Mentor ship, and Workshops to ensure fast-track learning.
- Led by collaborative Faculty from Academia, Industry and Global Blue chip Institutions.
- Competitive and affordable pricing.
Yes! This Program will help you in getting the Job, Last 2 days of the program is designed to connect you the Industry.
- 64-bit Operating System
- 2 GB RAM, 4 GB RAM Recommended
Online is recommended as you don't need to travel.
- Course Completion Certificate
All the classes Online or Offline are recorded and will be shared on daily basis to every Students .