Pantech eLearning

Free Embedded Systems Masterclass

Carrier Based Complete Data Science Training: Algorithms, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning
 Pre-Process &  Understanding Data
  • ✓ The course provides the entire Knowledge  you need to become a data scientist
  • ✓ Fill up your resume with in demand  data science tools & skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • ✓ Learn how to pre-process data Understand the mathematics behind Machine Learning
  • ✓ Start coding in Python and learn how to use it for statistical analysis

Who must take this course

  • ✓ Want to Master skill on the field of Data Science
  • ✓ Need to build more projects on Data Science
  • ✓ Need to build better portfolio to get job in Top MNC
  • ✓ Need to master the skill on In-demand field which has opportunity in the future
  • ✓ Want to Master skill on the field of Data Science
  • ✓ Need to build more projects on AI
  • ✓ Need to build better portfolio to get job in Top MNC
  • ✓ Need to master the skill on In-demand field which has opportunity in the future
  • ✓ To get great insights from the data which you working in
  • ✓ Want to make career change to the filed of Data Science for High paying Job
  • ✓ To solve their problem related to data using Data Science
  • ✓ Building solutions to real world problems
  • ✓ Average Data Science engineer salary is ₹ 9.0 LPA – Best career in the Future
  • ✓ Easy to learn, No prior knowledge required
  • ✓ More Hands-on project needed to be done to crack interviews

Course Syllabus

  • > Essential Python concepts for data science, including data types, variables, loops, and functions
  • > How to work with data using Python’s powerful data manipulation libraries, such as NumPy and Pandas
  • > How to visualize data using Python libraries such as Matplotlib and Seaborn 
  • > How to visualize data using tools such as Tableau and Power bi
  • > Machine learning with Python: Supervised, Unsupervised & Reinforcement Learning 
  • > Techniques and best practices for effective data analysis and data storytelling
  • Python Basics
  • Python Data Structures
  • Python Fundamentals
  • Advance Python (oops)
Pandas  for Data Science
NumPy for Data Science
Matplotlib
Seaborn
Scikit-Learn
NLTK
Keras
Tableau – Data visualization
Tableau – Data Sources, Worksheet
Introduction power BI 
Visualize Data in the Form of Various Charts, Plots, and Maps BI tools – Power BI
Google Colab Notebook
Python – Date and Time, Data Wrangling
Python – Data Aggregation
Python – Word Tokenization, Stemming, and Lemmatization
Python – Data Visualization
Python – Statistical Analysis
Python – Types Of Distribution
Python – Correlation, Chi-Square Test, Linear Regression
Supervised Learning – Classification and Regression
Liver disease Prediction – (Logistic Regression)
Crime Analysis – (KNN Algorithm)
Classifying muffins and cupcakes – (SVM Algorithm)
Fake news detection – (Naïve Baye’s)
Android malware – (Decision tree)
Credit card Fraud detection – (Random Forest)
Evaluating the classification model – (Confusion matrix) Calculating the accuracy score.
Classification model selection For Breast Cancer – (Classification)
Employee Salary Prediction – (Linear Regression Single Variable)
Advertisement and Sales Prediction – (Multiple Linear Regression)
Generating Data points based on some equation – (Polynomial Regression)
Ice – Cream shop revenue prediction from temperature – (Decision Tree Regression)
Agriculture Price Prediction – (Random Forest Algorithm)
Evaluating the performance of my regression model – ( Root mean square error and R2 score)
Regression Model selection for sales forecasting.
Unsupervised Learning – Clustering
Crime Pattern Analysis – (K-Means Clustering)
Customer Spending Analysis – (Hierarchical Clustering)
Flower Species – Data Visualization
Image Compression Using – SVD (Singular Value Decomposition)
Unsupervised Learning – Association
Market Basket Analysis – (APRIARI)
Market Basket Optimization / Analysis – (ECLAT)
Reinforcement Learning
Web Ads click-through rate optimization – (Upper Bound Confidence) 
Natural Language Processing
Hate Speech Detection – (NLTK)
Loan Prediction Problem – (XGBoost)
Deep Learning
Movie Review Classification – (RNN)
Digits Classification – (CNN)
AI – Cart Pole (Reinforcement Learning)

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