Machine Learning

( Duration: 5 Days )

The Machine Learning training course teaches experienced developers, architects, and team leaders the foundations of building machine learning solutions. Starting with data handling, going through data exploration, and finishing with an algorithms overview, this course provides delegates with the knowledge of modern tools and the appropriate way of thinking that are crucial for being successful with machine learning. Delegates then take their skills to the next level with the foundations of neural networks, deep learning, Keras, and TensorFlow to develop robust deep learning solutions.

By attending Machine Learning workshop, delegates will learn to:

  • Understand what is Machine Learning and which problems does it tackle.
  • Understand the Data Science pipeline.
  • Become familiar with the various tools in the Python ecosystem to handle data (clean and transform).
  • Understand the basics of descriptive statistics and probability for data exploration.
  • Using visualization libraries for gaining insight into the data.
  • Become familiar with using the wide variety of machine-learning algorithms.
  • Become familiar with Neural Networks
  • Gain an understanding of Machine Learning
  • Become familiar with Keras
  • Build models using TensorFlow
  • Understand Convolutional Neural Networks
  • Understand Recurrent Neural Networks

This Machine Learning course is for Analysts, Data Scientists and Software Developers




  • What is Data Science?
  • The Role of Machine Learning
  • Use-cases
  • Quality of Data

Data Handling

  • NumPy Essentials
  • Introduction to Pandas
  • Working with DataFrames
  • Filtering
  • Vectorized Operations

Descriptive Statistics

  • Measures of Center
  • Measures of Dispersity
  • Correlations
  • Z-Test
  • Pandas and Descriptive Statistics


  • Introduction to matplotlib
  • Creating Charts
  • The Seaborn Library
  • Complex Figures

Data Science

  • Introduction to Data Exploration
  • Scaling
  • Feature Selection
  • Feature Engineering

Algorithms Overview

  • Introduction to SKLearn
  • Supervised Learning
  • Regression vs. Classification
  • Overfitting vs. Underfitting
  • Algorithms
    • Linear Regression
    • Logistic Regression
    • Decision Tree (Bagging/RandomForest/GradientBoost)
    • K-Nearest Neighbors
    • Support Vector Machines (SVC/SVR)
  • Introduction to Unsupervised Learning
    • PCA
    • K-Means

Testing and Scoring

  • Overview of Scoring Methods
  • Cross-Validation

Neural Networks

  • Foundation for Neural Networks
  • Relus
  • Backpropagation
  • Regularization (L1, L2)

Deep Learning

  • What is Deep Learning?
  • Fully Connected Models
  • Using Keras and TensorFlow
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Text Processing
  • Loss Functions
  • Custom Architectures
  • Experiencing with TFLearn API
  • Discussion of well-known Architectures

Introduction to Reinforced Learning

Encarta Labs Advantage

  • One Stop Corporate Training Solution Providers for over 4,000 Modules on a variety of subjects
  • All courses are delivered by Industry Veterans
  • Get jumpstarted from newbie to production ready in a matter of few days
  • Trained more than 50,000 Corporate executives across the Globe
  • All our trainings are conducted in workshop mode with more focus on hands-on sessions

View our other course offerings by visiting http://encartalabs.com/course-catalogue-all.php

Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.