CERTIFICATION COURSE
No. of Hours: 78 |
Certificate Validity: 2 Yr(s) |
Introduction
This is an integrated program in Data Science and Machine Learning primarily designed for professionals pursuing career as Data Scientist, AI Engineers, Data Analyst and ML Engineers.
Our customized Data Science and Machine Learning Courses are designed by Data Scientists who are working on ML projects in our company. This program will help you achieve proficiency in Python programming language, data analysis, data pre-processing, machine learning algorithms, classification systems, regression modeling, clustering algorithms, recommender systems, and natural language processing to work on real world projects and case studies. These skills will help you prepare for the role of the Data Scientist.
Why to enroll for this course?
There is a huge requirement of Data Scientists in almost all domains including IT, Automobile, Telecommunication, Services, Construction, Security, Retails etc. and this is a major concern for Top MNCs organizations around the world. The major corporations are ready to pay top salaries for professionals with the good Data Science skills. This Data Science and Machine Learning Course equips with all the latest technologies Python, Data Analytics, and NLP. Thus you can easily take your career to the next level after completion of this certification course.
Prerequisite for learning
There are no special prerequisites required for Data Science and Machine Learning training. However, the knowledge of the following will make it easier:
- Basic Mathematics
- Simple Statistics
- Knowledge of programming language
- Business domain knowledge
Who will be our trainers ?
You can check the company and trainers profile here.
Course Contents
- Python Programming
– Python Installation and Basics
– Syntax and programming Structures
– Variables, Operators, Keywords, Expressions
– Decision Making: if, elif, else
– Loops: while, for, break, continue, pass
– List, Tuple, Dictionary, Set
– Functions, Modules
– Object Oriented Programming - Introduction to Data Science
– Detailed overview of data science, data scientists and its scope. - Introduction to Machine Learning
– How and what is ML with uses, types, applications - Python for Data Science
– numpy: Numerical Python
– pandas: Python data analytics with data frames object - Data Visualization
– matplotlib: creating different graphs
– seaborn: advanced visualization - Machine Learning Initiation
– Using ML in Python
– Data Pre-processing operations and their requirements - Machine Learning Algorithms: Regression
– Linear Regression
– Multiple Regression
– Polynomial Regression
– Decision Tree Regression
– Random Forest Regression
– SVM Regression - Machine Learning Algorithms: Classification
– Logistic Regression
– Decision Tree Classification
– Random Forest Classification
– SVM Classification
– KNN Classification
– Naive Bayes Classification - Ensemble Learning Techniques
– Bagging, Boosting and Stacking - Semi-Supervised Learning
– What, why and When ?
– Label Propagation
– Label Spreading - Dimensionality Reduction Techniques
– Principal Component Analysis
– Linear Discriminant Analysis - Machine Learning Algorithms: Clustering
– K-means Clustering
– Agglomerative Clustering
– Gaussian Mixture - Machine Learning Algorithms: Association Rule Mining
– Apriori Algorithm - Natural Language Processing
– Text mining with nltk
– Combining NLP and ML - Introduction to Neural Network
– MLP Classifier (ANN) - Graphical User Interface Design
– Tkinter programming
– Widgets of Tkinter
– Event handling and Layouts - The Recommender Systems
– Collaborative Filtering
– Content Based Filtering - Outlier Detection and Removal
– Methods of Detecting Outliers
– Boxplot, Clustering
– Isolation Forests, One Class SVM - Cross Validation Techniques
– K-fold Cross Validation
– Stratified K-fold Cross Validation
– LOOCV
– GridSearchCV - Pipelining and Regularization
– Pipelining
– L1 Regularization
– L2 Regularization
– Ridge and Lasso Regression - Resampling Techniques
– Random Under Sampling
– Random Over Sampling
– SMOTE - Correlation Techniques
– Pearson Correlation (r)
– Spearman Correlation (rho)
– Kandall’s Correlation (t)
– Using numpy, scipy, pandas
– Regression Analysis - Time Series Analysis
– The time series dataset
– Facebook Prophet package - Project Development
– Overview of Data Science project
– Choosing stages of Project
– Applying the algorithms
– Development and User Interface
Configuration:
1. Operating System: Ubuntu 20.04 LTS
2. RAM: 4 to 6 GB
3. Processor: i3 (Minimum)
Online Timings:
B1: 6 Hrs per Week (Friday to Sunday) or
B2: 8 Hrs per Week (Monday to Thursday) or
B3: 6 Hrs per Week (Saturday and Sunday)
Total: 78 hrs.
Weekly Assignment:
1. After every weekend session you’ll need to solve assignments until next week.
2. On completion of all the modules, you are required to appear exam.
3. All exams will be taken either on Center / Off site. When Off-site you will submit assignments via mail.
Final Exam:
A duration of 30 days, will be given after completion of Course
The Final exam will be a conducted once, in case of Failure; exam can be reappeared after another 1 month
Opportunities:
As first Opportunity after Passing exams, you will deployed on a Well monitored Live Project.
You are required to resolve the Problem statement with Expected outputs, with that you will be receiving a token of Completion Letter of respective Project.