machine learning advance

Course Features

Skill level:

Advance

Duration:

 5 Months

Projects:

 Yes

Practical Ratio:

 30 : 70

Assessments:

 Yes

Quizzes:

 Yes

Course Details

The course aims to train engineers to become experienced data scientist. It will contain lots of practical, theory & many end- to-end data science implementation in depth.

Prerequisite

Language: Python or R.
Theory: Machine Learning Concepts.

Course Outcome

At the end of the course, you should be able to :

  • Apply a range of advanced machine learning techniques including tuning & regularizing these models.
  • Evaluate rigorously the performance of statistical models, & justify the selection of particular models for use.
  • Gain a deep insight of NLP ,Recommender System & an overview of Deep Learning.

Curriculum

  • Setting up Anaconda & Python Notebooks
  • Python Data Science ecosystem Introduction
  • Working on notebooks for Data Science
  • Python Scientific libraries Numpy, Scipy, Scikit-learn, Matplotlib
  • Exploratory Analysis
  • Distributions CDF, PDF
  • Distribution Modeling
  • Hypothesis Testing
  • Data Cleaning & Wrangling
  • Data Compression & Dimensionality Reduction
  • Missing Values & Outlier
  • Feature Generation & Importance
  • Gradient Descent
  • Linear Regression
  • Logistic Regression
  • Regularization
  • Multiple Variables
  • Decision Trees
  • Ensemble Modeling
  • Random Forest (Bagging)
  • Boosting Trees ( Boosting)
  • XGBoost
  • Data Split Practices
  • Cross Validation
  • K-Fold Validation
  • Confusion Matrix
  • ROC Curves
  • Mean Absolute/Square Errors & R-Square
  • Neural Networks
  • Regularization
  • Hyperparameters Tuning
  • Support Vectors
  • Support Vector Classifier
  • Tuning C & Gamma
  • Apriori Algorithms (Discovering Association Rules)
  • Market Basket Analysis (Cross Selling/ Up Selling)
  • RFM Analysis
  • Collaborative Filtering based Recommendation
  • Trend & Seasonal Analysis
  • Different Smoothing Techniques
  • ARIMA Modeling
  • ETS Modeling
  • NLP Introduction
  • Text Pre-processing & cleaning
  • Tokenizing Text, Noise Entity Removal, Stemming & Lemmatization
  • Part of Speech Tagging, TF – IDF
  • Named Entity Recognition, N-Grams
  • Topic Modeling
  • Tools & Core API’s used in Industry
  • NLP based Industry Use Cases
  • Text Classification
  • Conversational System(Chat-Bot)
  • Text Similarity
  • Deep Learning Concept & why ?
  • Deep Networks, Cross Entropy, SoftMax, Regularization
  • CNN, RNN, LSTM, Encoders
  • Image Classification & Segmentation Demo