Pune Branch
+91 9028000415
+91 7028203078
PCMC Branch
+91 9822907307
+91 9822907407
plc scada automation training institute
SAGE didactic Austrelia
Pune Branch
+91 9028000415
+91 7028203078
Pimpri Branch
+91 9822907307
+91 9822907407

Data Science - Machine Learning and Deep Learning

Data science is an essential part of many industries today, given the massive amounts of data that are produced. Its popularity has grown over the years and companies have started implementing data science techniques to grow their business and increase customer satisfaction. Machine learning is the backbone of Data science.

Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science which includes statistics and predictive modeling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier. Data scientists need to understand deep learning techniques and use them especially when dealing with massive data.

Data scientists are in high demand and well-paid because they work in both the business and IT sectors.

Duration : 4.5 Months

Ideal for : Freshers and professionals with 1-2 years of Industry experience who want to start their career in IT Sector as Data Analyst, Data Engineers or Data Scientist for various domains of Data science, Artificial Intelligence and Machine learning.

Aims at helping you master all the basic and advanced level skills that are crucial in the field of Data Science & Machine Learning

Designed for Working Professionals & Freshers

Course designed by Industry Experts as per Industry need

Job Oriented Programme – Kickstart your career in IT Sector

Various assignments on each important topic

Real Time Projects

Mock interviews and Mock tests preparation

Placement Assistance

Course Content

  • What is Python? History of Python.
  • Python Data Types
  • What are Keywords, What are variables
  • Introduction to Python
  • Control Flow Statements
  • Data Structures
  • Functions
  • Object oriented Programming
  • Exception Handling and GUI
  • Project Discussion
  • What is SQL?
  • Why we need SQL ?
  • What is Database Management System (DBMS)?
  • Types of DBMS
  • Execution of SQL Query
  • Difference between SQL & MYSQL
  • Introduction to MYSQL
  • Installation of MYSQL Server
  • Download Sample Database
  • Basic SQL Keywords
  • Joins
  • DML / DDL
  • Introduction
  • Distributions and Various
  • Referential Statistics
  • Case Studies
  • 1. Industry Case Studies
    • OLA Ride Time Prediction
    • Gmail Spam/Not Spam Prediction
    • Amazon Reviews Sentiment Predictions
    • DataScience Vs DataAnalysis Vs MachineLearning Vs Deep Learning
    • Introduction to Numpy, Pandas, Sci-kit Learn and
    • Matplotlib Library
    • Importing data from different Sources
  • 2. Basic Terminologies and Basic Maths
    • Traditional Programming Vs Machine Learning
    • Types of Machine Learning Problems
    • Supervised and Unsupervised Learning
    • Classification and Regression
    • Over fitting and Under fitting
    • What is a point and a Vector?
    • Distance between 2 points, Distance between a point and a line.
    • Equation of a line, Equation of a Plane, Equation of a hyperplane
    • .
    • Dot product and Projection of one vector onto another.
    • Basics of Differentiation
    • KNN(K nearest Neighbour) Algorithm
    • Geometric Intuition of KNN
    • Mathematical Intuition of KNN
    • Limitations of KNN
    • What are Hyper-parameters?
    • Hyper-parameters Tuning
    • Why do we need Cross-Validation?
    • Code Walkthrough on KNN
  • Supervised Learning continues
    • Naive Bayes algorithm
    • What is Conditional Probability
    • What is Naive about Naive Bayes?
    • Geometric Intuition of Naive Bayes
    • Mathematical Intuition of Naive Bayes
    • Limitations of Naive Bayes
    • Hyperparameter Tuning in Naive Bayes
    • Code Walkthrough of Naive Bayes
    • Introduction to Logistic Regression
    • Geometric Intuition of Logistic Regression
    • Mathematical Intuition of Logistic Regression
    • Why do we need sigmoid function?
    • Regularisation (L1 and L2)
    • Limitations of Logistic Regression
    • Code Walkthrough of Logistic Regression
    • Introduction to Linear Regression
    • Geometric and Mathematical Intuition
    • Assumptions of Linear Regression
    • Limitations of Linear Regression
    • Code Walkthrough of Linear Regression
    • Optimisation Theory
    • Convex and Non Convex Functions
    • Gradient Descent , Stochastic Gradient Descent
    • Introduction to SVM (Support Vector Machine
    • Geometric Intuition
    • Mathematical Intuition
  • Decision Tree and Assembles
    • Decision Tree
    • Geometric Intuition of Decision Tree
    • Mathematical Intuition of Decision Tree
    • Entropy and Gini Impurity
    • Information Gain
    • Limitations of Decision Tree
    • Code Walkthrough of Decision Tree
    • What is Ensembles
    • Bagging and Boosting
    • What is Ensembles?
    • Bagging and Boosting
    • Concept of Bootstrapping
    • Introduction to Random Forest
    • Variance and Bias
    • Geometric Intuition of Random Forest
    • Why Random Forest is so famous?
    • Code Walkthrough of Random Forest
  • Performance Matrix & Different Situations in Supervised Learning
    • Accuracy
    • Why Accuracy as a metric will fail in most of the real world cases?
    • Precision and Recall
    • F1 Score
    • Confusion Matrix
    • Log-loss
    • ROC-AUC Curve
    • RMSE(Root Mean Square Error)
    • R2(Coeficient of Determinant)
    • MAD(Median Absolute Deviation)
    • How to Handle Outliers in the data?
    • How to deal with the imbalance data?
    • How to handle categorial data?
    • Scaling of Features
    • Curse of Dimensionality
  • Unsupervised learning & Dimension Reduction
    • What is Unsupervised Learning?
    • What is Clustering?
    • K-Means Clustering
    • Hierarchal Clustering
    • Why Dimensions Reduction?
    • PCA(Principle Component Analysis)
  • Machine Learning Project
    • Business Problem
    • Contraints
    • Data Collection
    • Formulate Business Problem to Machine Learning
    • Problem , Data Cleaning
    • Data Preprocessing
    • EDA(Exploratory Data Analysis)
    • Modelling
    • Evaluating the Performance of the models
    • Retrain if necessary , Deployment
  • History of Neural Networks
    • Who invented Neural Network?
    • What is the intuition of a Neural Network?
    • What is a perceptron?
    • Connecting Logistic Regression, Linear
    • Regression with Perceptron
    • Multi Layer Perceptron
    • Training of a Perceptron
  • MLP Backpropogation
    • Notation
    • Training a MLP:Chain Rule
    • Training a MLP:Memoization
    • Backpropogation
    • Activation Functions
    • Sigmoid
    • Tanh
    • RELU
    • Vanishing gradient
  • Deep Multilayer Perceptrons
    • Dropout and Regularisation
    • Batch Normalisation
    • Batch SGD with Momentum
    • Adam
    • Softmax and Cross-Entropy
    • How to train Deep MLP?
    • Tensorflow and Keras Overview
    • Install Tensorflow
    • Softmax Classifier on MNIST data
    • Code Walkthrough of MLP
    • Hyperparameter Tuning in Keras
  • Introduction to CNN
    • Introduction to CNN(Convolution Neur
    • What is Convolution?
    • Convolution:Padding and Stride
    • Convolution over RGB image
    • Max Pooling
    • CNN Training
    • AlexNet
    • VGGNet
    • Residual Network
    • Inception Network
    • What is Transfer Learning?
    • Code Walkthrough of CNN
  • Introduction to RNN
    • Why RNN (Recurrent Neural Network)?
    • Training RNN
    • Types of RNN
    • Need of LSTM
    • LSTM(Long Short Term Memory)
    • Deep RNN
    • Bidirectional RNN
    • Code Walkthrough of RNN
  • Introduction to NLP
    • What is NLP (Natural Language Processing)?
    • BOW (Bag of Words)
    • Text Preprocessing:Stemming and Lemmatisation
    • Stop Word Removal
    • Tokenisation
    • Unigram, Bigram, Ngrams
    • TF-IDF
    • Weighted TF-IDF
    • Word2Vec(W2V)
    • Code Walkthrough of NLP Techniques
  • Deep Learning Project
    • Business Problem
    • Contraints
    • Data Collection
    • Formulate Business Problem to Deep Learning Problem
    • Data Cleaning
    • Data Preprocessing
    • EDA(Exploratory Data Analysis)
    • Feature Extraction
    • Modelling
    • Evaluating the Performance of the models
    • Retrain if necessary
  • Introduction to Power BI
    • What is PowerBI? Why PowerBI?
    • Power BI Desktop – Install
    • Data Sources and Connections
    • Connect to Data in Power BI Desktop
    • How to use Query Editor in Power BI
    • Why Data Visualization
    • How to use Visual in Power BI
    • Charts in Power BI (Scatter, Waterfall, Funnel)
    • Slicers
  • Data Analysis Expression
    • What is DAX(Data Analysis Expression)?
    • Data Types in DAX
    • Calculation Types
    • DAX Functions : Date and Time, Time Intelligence,
    • Information, Logical, Mathematical, Statistical,
    • Text, Aggregate Measures in DAX
    • Table Relationships and DAX
  • Custom Visualisation
    • What Are Custom Visuals?
    • Office Store
    • Downloading Custom Visuals
    • Importing Custom Visuals in Power BI Report KPI
    • Visuals
    • Data Binding in Power BI
  • Power BI Embedded
    • Power BI Embedded Conceptual Model
    • Workspace Collection
    • Adding Power BI content to a Workspace
    • Application Authentication Tokens
    • Parts of Power BI embedded - REST API
    • Power BI Embedding without an Embed Token
    • Power BI Embedding with an Embed Token
    • PROJECT(Creating a Dashboard)

Kick-start your career