Make A Mark In The Exciting World of Artificial Intelligence!

Datalore Labs, one India’s reliable Data Science Education & Training provider, is offering a special three-month course in Applied AI at their Bengaluru headquarters. The extensive course is aimed at making you job-ready and embolden you to explore a bright career in Data Science or AI.

What should you expect?

Applied AI courses offered by Datalore Labs are designed by industry veterans as learning experiences that will support your journey from the very first exercise to your first step in the career. We have dedicated full-time mentors with over decades of experience in Data Science who deliver industry-needed concepts with an emphasis on hands-on experience. Students, who emerge as best performers, will also have opportunity to work with Datalore Labs. We will have an agreement of two years with the top performers, to work on our in-house as well as client projects. Other participants will also get an opportunity to work with our partners or will be provided career assistance through as many as four interviews. We will help the participants to prepare for the interview. However, clearing the interviews will be completely based on individual’s hard work.

Key Features

  • Post Graduate Program Certification
  • Learn from industry veterans
  • 200+ hours of Blended Learning
  • Partner network AWS and Intel
  • 100% Placement Assistance

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What do we expect?

We encourage students to make efficient use of our learning management system (LMS) and learning resources. In order to become eligible for our job interview programme, we expect dedication, regular in attending sessions and hard work from students for the next three months. Participants will be screened in which we will test their eligibility and skills once the course is complete. If the participant puts hard work and pass the test we will be able to help with the placement.

Program Details

Data Science is a new rapidly evolving field that demands an array of skill sets in mathematics, statistics, machine learning, deep learning and software engineering. Individuals who can align these multi-functional competencies with their existing area of knowledge will prove indispensable for data-driven businesses.

Our training programs are developed by data scientists and machine learning engineers, with years of experience and multi-functional competencies. Our training covers industry-relevant developments in the field of machine learning and deep learning. Through our workshops, you can gain the expertise necessary to become a competitive data scientist

Module 1: Python for Data Science

Python Fundamentals

  • Introduction to Jupyter Notebook
  • Data Types in Python
  • Data Structures in Python
  • Controlstructures,Loops and Conditional statements in Python
  • Python Functions
  • Basics of Object-Oriented Programming in Python
  • Python Packages and Modules

NumPy and Pandas Foundations

  • Multi-dimensional arrays (numpy arrays) in Python
  • Create and use NumPy Arrays.
  • NumPy array operations
  • Pandas series and dataframes
  • Loading and writing files using Pandas
  • Data manipulation using dataframes
  • Data aggregation using Pandas

Matplotlib and Seaborn Foundations

  • Introduction to data visualization
  • Various types of plots – univariate and multivariate
  • Matplotlib plots
  • Seaborn plots
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Module 2: Data Wrangling

Data Wrangling – Part 1

  • Data loading from various sources
  • Summary statistics
  • Data exploration
  • Dealing with missing values
  • Reshaping the data
  • Filtering data for required conditions

Data Wrangling – Part 2

  • Data Manipulation
  • Outlier detection and removal
  • Feature engineering
  • Discretization
  • Data transformation
  • Encoding techniques
  • Grouping and aggregation
  • Data normalization

Exploratory Data Analysis

  • Introduction to Statistics
  • Descriptive Statistics
  • Inferential Statistics
  • Probability and Expectations
  • Sampling Distribution
  • Univariate analysis
  • Bivariate analysis
  • Multivariate analysis
  • Data distribution
  • Patterns in the data
  • Basic plotting to extract meaningful information from data
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Module 3: Machine Learning

Introduction to Machine Learning

  • Introduction to ML Problems
  • ML terminologies
  • ML project workflow
  • ML real life examples

Supervised Learning – Regression

  • Regression Modeling
    • Introduction
    • Modeling concept
    • Example problem - Housing price
  • Simple Linear Regression
    • Error metric - SSE, MSE, RSquared
    • Least Square algorithm
    • Gradient DescentAlgorithm
    • Implementation using scikit-learn
  • Multiple Linear Regression
    • Dummy variables
    • Error metric - SSE, MSE, RSquared
    • Gradient Descent Algorithm
    • Feature Selection (Incremental)
    • Implementation using scikit-learn

Supervised Learning – Classification

  • Classification Modeling
    • Introduction to Classification Models
    • Error Metrics: Accuracy Score, Confusion Matrix
    • Type1 and Type2 errors
    • Decision boundaries
  • Logistic Regression
    • Discrete outcomes
    • Logit function
    • Probability scores
    • Implementation using scikit-learn
  • DecisionTrees
    • Entropy
    • Using Entropy in classification
    • Information Gain
    • Tree pruning
    • Implementation using scikit-learn
  • Random Forests
    • Bias variance errors
    • Ensemble models
    • Randomness in Random Forest
    • Hyper parameters
    • Implementation using scikit-learn

Unsupervised Learning – Clustering

  • Cluster Modeling
    • Introduction to clustering
    • Distance measures
    • Error metrics
    • Analysing cluster outputs
  • Hierarchical Clustering
    • Agglomerative method
    • Divisive method
    • Understanding Dendrogram
    • Cutting the dendrogram for obtaining clusters
    • Implementation using scikit-learn
  • K-Means Clustering
    • Distance measures
    • Centroids and their importance
    • Steps involved in K-Means
    • Local optima problem
    • Implementation using scikit-learn
  • Dimensionality Reduction
    • Principal component analysis
    • Orthogonal transformation
    • Feature selection using PCA
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Module 4: Deep Learning

Introduction to Deep Learning and ANNs

  • Introduction to Deep Learning and its Applications
  • Basics of Linear Algebra
  • Basics of Calculus

    • Artificial Neural Networks

    • Introduction
    • Architecture – Layers, weights and neurons
    • Computation in a single neuron
    • Activation of neurons
    • Training (feed forward and back propagation steps)
    • Implementation using Keras

    Convolutional Neural Networks (CNNs) and its Applications

    • Introduction to computer vision
    • CNN architecture
    • Convolution filters
    • Loss function
    • Training CNN models
    • Image classification example using Keras

    Recurrent Neural Networks (RNNs) and its Applications

    • Introduction to sequence prediction problems
    • Types of sequence prediction problems
    • RNN architecture
    • Training RNN models
    • Time series prediction example using Keras

    Long Short-Term Memory (LSTMs) and its application

    • LSTM architecture
    • Various gates in a LSTMlayer
    • Training LSTM models
    • Time series prediction example using Keras

    Autoencoders and its application

    • Introduction
    • Non-Linear dimensionality reduction
    • Encoders and Decoders
    • Training an autoencoder
    • Denoising images using Autoencoders
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Tool Covered

Program Advisor

Sudheendra

Director

He holds BE Biotechnology and PG Diploma in Bioinformatics degrees. He has 12+ years of experience in executing and managing analytics and machine learning projects related to life sciences and telecommunication at Accenture and Monsanto India Limited.

Addmision Details

Application Process

The application process consists of three simple steps. An offer of admission will be made to the selected candidates, which they would accept by paying the admission fee.

Submit Application

Interested candidates register to the program online providing the relevant and accurate details

Application Review

An admission panel will shortlist candidates based on the application details and short telephonic screening

Admission

Selected candidates will enroll for the program by paying admission fee and program will begin within 1-2 weeks

Admission Fee & Financing

Special discounted price for the complete course bundle on Applied AI (includes all the above mentioned individual programs in one package). Speak to expert.

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Build your career as a Data Scientist

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