Statistics:

• Fundamentals of Math and Probability
• All about Population & Sample
• Introduction to Statistics, Statistical Thinking
• Descriptive Statistics
• Inferential Statistics
• Hypothesis Testing
• Linear Algebra
• Data Processing & Exploratory Data Analysis
• EDA
• Statistics Assignments: Total 4 practice sets and Assignments from Statistics

Machine Learning:

• Introduction to Machine Learning
• Regression and Classification Models
• Linear Regression Model
• Data Preprocessing
• Encoding the Data
• Logistic Regression Model
• Evaluation Metrics for Classification model
• K Nearest Neighbours Model
• Decision Tree Model
• Random Forest Model
• Hyperparameter Tuning
• Naive Baye’s Model
• Case Study on Kart Model Business & Random Forest
• K Means and Hierarchical Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA):
• Support Vector Machine (SVM)
• Case Study on Recommendation Engine for e-commerce/retail chain & Twitter data analysis using NLP

SQL:

• SQL and RDBMS
• Advance SQL
• NoSQL, HBase & MongoDB
• JSON Data & CRUD
• Programming with SQL

MongoDB:

• Introduction to MongoDB
• MongoDB (Advance)

Tableau:

• Introduction to Tableau
• Visual Analytics
• Dashboard and Stories
• Tableau (Advance)
• Hands-on: Connecting data source and data cleansing
• Working with various charts, Deployment of Predictive model in visualization

PowerBI:

• Getting Started With Power BI
• Programming with Power BI

Big Data & Spark Analytics:

• Introduction To Hadoop & Big Data
• What is Spark
• Getting to know PySpark
• Hands-on: Map reduce Use Case: Youtube data analysis & Spark RDD programming

Time Series:

• Introduction to Time Series Forecasting
• Introduction to ARIMA Models
• Regex pattern and its interpretation.
• Case Study on Time series classification of smartphone data to predict user behavior
• Performing Time Series Analysis on Stock Prices & Time series forecasting of sales data

Deep Learning Using Tensorflow:

• Introduction to Deep Learning And TensorFlow
• TensorFlow Classification Examples
• Understanding Neural Networks with TensorFlow
• Convolutional Neural Network (CNN)
• Project on Building a CNN for Image Classification
• Introducing Recurrent Neural Networks skflow: RNNs in skflow
• Understanding of TFLearn APIs
• Understanding Keras API for implementing Neural Networks
• Real-Time Project on SPAM Prediction using RNN & Image Classifier using PyTorch

Natural Language Processing (NLP):

• Natural Language Processing
• Text Analysis
• KNN
• Use cases on NLP: Sentiment analysis for marketing
• Text Pre-Processing Techniques
• Stemming
• Projects And Case Study on Sentiment analysis for Twitter, web articles & Advanced Text Analytics & NLP

Module 3 Computer Vision:

• Computer Vision overview
• Image Filtering
• Image Processing
• Image Classification and segmentation
• Project: The Problem of Scale and Shape
• Real-Time Use Case: Single Shot MultiBox Detector & Object Localization

Reinforcement Learning:

• What is Reinforcement Learning - Basics
• Approximation Methods for Reinforcement Learning
• Projects and Case Studies on Solving Taxi Environment & Solving Frozen Lake

Model Training & Deployment Using (AWS GCP):

• AWS (Amazon Web Services)
• GCP (Google Cloud Platform)
• Introduction to AWS and GCP Cloud ML Engine
• Deploying Machine Learning Model
• Training Machine Learning Mode

• Machine Learning Engineer
• Data Scientist
• NLP Scientist

• Software Developer/Engineer (AI/ML)
• Human-Centred Machine Learning Designer