360special

Course Features

Skill level:

Intermediate

Duration:

 8 Months

Projects:

 Yes

Practical Ratio:

 30 : 70

Assessments:

 Yes

Quizzes:

 Yes

Course Details

This special course is designed for students & working professionals who want to step their career in Artificial Intelligence & be part of the elite digital community with lucrative salary packages & exciting work opportunities. The course is focused from foundation skills like SQL, Python, Unix etc to in depth knowledge of Machine Learning & Deep Learning algorithms & with a lot of practical hands-on & project works aligned with the industry use cases & challenges. The participants would be also trained on Communication Skills & all necessary things like Resume Building, Interview Preparation would be taken care by 360DT team. This is a Job Guarantee course offered by 360DT, where in a participant needs to clear an assessment criterion with eligibility conditions.

Prerequisite

The participant should have good analytical skills, working knowledge of any Programming language & attitude to learn is essential for this course.

The participants would undergo an assessment first & only then they would be eligible for this special course that offers 100% Job Guarantee.

Course Outcome

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

  • The participant would have a Offer letter from a reputed organization
  • Ability to build AI Models using Python Scikit Learn, Tensorflow & Big Data Spark ML.
  • Extensive hands-on experience with a few capstone projects & lot of practice.
  • Well equipped with fundamentals & in depth concepts of Machine learning & Deep Learning.

Curriculum

  • What is RDBMS & SQL
  • DDL & DML Operations in SQL
  • Complex features like Joins, Subqueries etc.
  • DCL Operations
  • Class hands-on followed by Home Assignments
  • Basics of Data Science & Machine Learning.
  • Different Tools – R, Python, Matlab etc.
  • Data Science Use Cases
  • Exploratory Analysis
  • Distributions, CDF, PDF
  • Distribution Modeling
  • Variable Relationships & Estimation
  • Hypothesis Testing
  • Regression & Linear Least Square
  • Analytic Methods
  • End-2-End cycle of ML Implementation
  • Data Cleaning & Wrangling
  • Data Compression & Dimensionality Reduction
  • Missing Values & Outlier
  • Feature Generation & Importance
  • ML for Feature Engineering & Pre-Processing
  • Gradient Descent
  • Linear Regression
  • Logistic Regression
  • Regularization
  • Multiple Variables
  • Time Series
  • Decision Trees
  • Random Forest
  • Boosting Trees
  • XGBoost
  • Support Vector Machines
  • Principal Component Analysis
  • Ridge Regression
  • Spectral Clustering
  • KNN
  • K-Means
  • Hierarchical Clustering
  • Data Split Practices
  • Cross Validation
  • K-Fold Validation
  • Confusion Matrix
  • ROC Curves
  • Mean Absolute/Square Errors & R-Square
  • Ensemble Learning & Model Stacking
  • Visualize Model Outputs & Intermediate Outputs
  • Python Tools for Visualizations
  • Setting up Environment
  • Pickle File Creation & loading
  • Flask/Django using Python.
  • Deploying & Prediction
  • 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
  • Maths Overview for DL
  • Current State of Tools & Platforms
  • Deep Learning Concepts
  • Activation Functions
  • Learning Mechanisms & Cost Functions
  • Gradient Descent
  • Regularization, Normalizations & Dropouts
  • Mini Batch & Optimization of Networks
  • Tensorflow Basics
  • Architecture with Estimator API
  • Packaging Tensorflow Code for Machine Learning Models
  • Tensorflow 2.0 Features – Eager Execution
  • Architecture; Pooling & FC Layers
  • Different Flavours of CNN
  • Layers optimization
  • Popular CNN Models
  • Attention Mechanism
  • Image Detection & Localization; Segmentation Networks
  • Object Detection at embedded hardware.
  • Transfer Learning
  • Using CNN’s for NLP
  • New Research & State-of-art’s
  • Basic Network Architecture
  • LSTM’s
  • GRU & Attention Models
  • Sequence Modelling with other RNN flavours
  • RNN & NLP
  • Encoder & Decoder
  • New Research & State-of-art’ s
  • DBN’s & RBM’s
  • Generative Adversarial Networks
  • Auto-Encoders
  • Clustering
  • Deep Reinforcement Learning
  • Overview of Big Data & Hadoop
  • Why HDFS & key concepts
  • Map Reduce
  • YARN Architecture
  • Sqoop, Hive & Impala
  • What is Apache Spark?
  • Using the Spark Shell
  • Resilient Distributed Datasets (RDDs)
  • Functional Programming with Spark using python
  • Iterative Algorithms
  • Graph Analysis
  • Machine Learning Algorithms