About the course

Machine learning has transformed the way business is taken into data-driven decisions. Machine learning is a specialized field that has enormous potential to grow owing to its demand in business. The increased need to handle enormous data and the use of connected devices using IoT using data reinforces the importance of machine learning in today’s dynamic world.  Knowledge of machine learning helps you to understand your customers better and increases your efficiency.

Our course in intermediate level machine learning will help you to gain the requisite skills and knowledge to tackle the real-world problems. Businesses and organizations need machine learning experts to solve their business problems in every sector and industry.  Enroll in our course and take a leap into a dynamic career of machine learning.

Machine Learning Intermediate Course overview

Our course in machine learning will help you to gain basic to intermediate level knowledge of the machine learning concepts. You will learn to solve any complex business with powerful machine learning models. You will begin learning this course from fundamentals to Python programming, supervised and unsupervised learning, Seaborn, Matplotlib, Scikit-Learn, SVM etc.

We have identified intermediate machine learning concepts to teach you in the best possible way. Once you have enrolled in our course, we will provide training videos, course learning materials, practical exercises to test your ability, case studies and real-world scenarios to gain hands-on experience on the subject. We have highly qualified and experts from the industry to conduct training. They will help you in every step of learning and make your learning journey smooth. You will get all the necessary training materials, interactive videos to help you understand the subject. We also provide 24/7 LMS support.

After the completion of the training:

• You will learn to setup an environment for Python development
• You will learn to solve real-world complex problems with machine learning tools
• You will understand various statistical techniques like classification, regression, machine learning algorithms, performance metrics like R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. You will learn when and how to use them.
• You will learn to use techniques and tools like bagging, boosting or stacking to combine models
• You will learn to understand the data set by using unsupervised Machine Learning (ML) algorithms like Hierarchical clustering, k-means clustering etc.
• You will learn to use machine learning in Jupyter (IPython) notebook, Spyder and various IDE
• You will learn to communicate visually and effectively with Matplotlib and Seaborn
• You will learn to create new features for improvement of algorithm predictions
• Make use of train/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen data
• You will learn to implement SVM technique for handwriting recognition, and classification problems.
• You will learn to implement decision trees to predict staff attrition
• You can implement association rules in retail sector

Prerequisites

To learn the intermediate level machine learning course, you must have basic knowledge of machine learning, statistics, programming languages like C, C++, Java. It is also expected that you should have robust knowledge of math and computer operations.

This course is suitable for:

• All the IT professionals who want to take off their careers in machine language domain.
• All the professionals having basic programming language skills and interested to explore the artificial intelligence and machine learning space.
• Any professional who want to transit to artificial language and machine language space.
• All the programmers who are willing to learn machine learning using Python programming language
• All the professionals who are interested and responsible for problem solving for real-world data problems.
• All the professionals who possess the basics of machine learning and want to move ahead with intermediate level of machine learning algorithms
• All the professionals who find working on advanced excel to handle large datasets.
• All the data experts who would like to present their data related interpretations with better visualizations and in a professional way.
• All the IT professionals who want to start their career as a data scientist.
• All the professionals who want to solve data-based problems to their domain

Machine Learning Intermediate Course Content

Introduction to Machine Learning

• Understand the applications of Machine Learning
• Understand the differences between Supervised vs Unsupervised Learning
• Learn to identify the Python libraries which are suitable for Machine Learning

Linear algebra for machine learning

• Introduction to N-Dimensional Arrays in Python
• Learn to Index, Slice and Reshape NumPy Arrays
• Understand and work with Vectors
• Learn the concept of Vector Norms
• Define and describe the concept of Matrices and work with Matrix Arithmetic
• Define and describe Matrix Types in Linear Algebra
• Learn to work with Matrix Operations to implement in Machine Learning
• Understand and implement Tensors in Machine Learning
• Overview of Matrix Factorization
• Overview of Eigen decomposition
• Overview of Singular-Value Decomposition (SVD)
• Overview of Principal Component Analysis (PCA)

Introduction to Regression

• Introduction to Linear Regression
• Introduction to Non-linear Regression
• Understand the various Model evaluation methods

Regression

• Overview of Scikit-Learn library
• Overview of exploratory data analysis (EDA)
• Understand the Correlation Analysis and Feature Selection in machine learning
• Learn to perform Linear Regression with Scikit-Learn library
• Learn and implement the Five Steps Machine Learning Process
• Identify and Learn the Regression techniques to perform machine learning
• Learn to evaluate performance in Regression Model
• Overview of Multiple regression
• Overview of Regularized Regression
• Overview of Polynomial Regression
• Understand how to deal with Non-linear Relationships
• Understand the feature Importance
• Understand the Data Preprocessing with regression analysis

Introduction to Classification

• Define and describe K-Nearest Neighbor
• Learn to work with Decision Trees
• Learn to work with Logistic Regression
• Understand and learn to work with Support Vector Machines
• Learn to evaluate Models with classifiers
• Introduction to Classification
• Understand the functions and objectives of Modified National Institute of Standards and Technology database (MNIST).
• Understand the concept of Stochastic Gradient Descent (SGD)
• Define and describe Performance Measure and Stratified k-Fold
• Understand the concept of confusion Matrix

Unsupervised Learning

• Overview of unsupervised learning
• Define and describe K-Means Clustering
• Define and describe Hierarchical Clustering
• Define and describe Density-Based Clustering

Unsupervised Learning: Dimensionality Reduction

• Understand the Dimensionality Reduction Concept
• Overview of Principal Component Analysis
• Introduction to Kernel Principal Component Analysis
• Understand the differences between Linear Discriminant Analysis vs Principal Component Analysis

Work with Anaconda

• Understand how to install applications and create an environment to work with Anaconda
• Learn to work with Error Messages
• Learn to read CSV Data into Memory
• Learn to load data from Seaborn
• Learn to visualize data in Anaconda

Python Basics

• Understand object-oriented programming in Python
• Introduction to NumPy & Pandas
• Learn Data Pre-processing in Python
• Learn to manipulate data in Python
• Learn to visualize Data in Python

Predictive analytics

• Learn the basics of Statistics to perform predictive analytics
• Learn the concept of Probability
• Learn Inferential Statistics
• Understand and learn Generalized Linear Models
• Learn advanced Regression concepts

Machine learning with Python

• Introduction to Machine Learning with Python
• Learn to work with regression techniques in Supervised Learning
• Learn to work with classification techniques in Supervised Learning
• Understand the concept of Model Selection and Boosting
• Overview of Unsupervised Learning
• Understand the concept of Dimensionality Reduction
• Learn the Association Rules in data mining and provide recommendation
• Understand the Time Series Analysis

Recommender Systems

• Learn Content-based recommender systems
• Understand and work with collaborative Filtering

Graphic models

• Introduction to Graphical model
• Overview of Bayesian Network
• Introduction to Markov’s Networks
• Define and describe Model learning

Reinforcement Learning

• Introduction and overview of Reinforcement Learning
• Learn to use Bandit Algorithms and Markov Decision Process appropriately
• Learn Dynamic Programming and Temporal difference learning methods in reinforcement learning
• Understand how to work with deep Q learning

Natural language processing

• Understand how to Pre-process text in Natural Language Processing
• Learn to analyze sentence Structure
• Learn to classify Text in natural language processing

Artificial intelligence and deep learning

• Introduction to Deep Learning
• Understand Neural Networks with TensorFlow
• Gain deep understanding of Neural Networks with TensorFlow
• Gain mastery in deep Networks concepts
• Define and describe Convolutional Neural Networks (CNN)
• Define and describe Recurrent Neural Networks (RNN)
• Understand the restrictions of Boltzman Machine (RBM) and Autoencoders
• Learn to work with API in Keras
• Learn to work with API in TFLearn

Ensemble Machine Learning

• Overview of Ensemble Learning Methods
• Define and describe Bagging
• Define and describe Random Forests and Extra-Trees
• Define and describe AdaBoost
• Learn how to work with Gradient Boosting Machine
• Overview of XGBoost
• Understand how to install XGBoost

Decision Tree

• Introduction to Decision Tree
• Understand how to visualize data in a Decision Tree
• Understand the Visualizing Boundary
• Understand regression in tree, regularization and over Fitting
• Understand the End to End Modeling with decision trees model
• Overview of XGBoost Basics
• Learn to prepare Data for Gradient Boosting with XGBoost in Python
• Learn to evaluate Gradient Boosting Models with XGBoost in Python
• Avoid Overfitting by Early Stopping with XGBoost In Python
• Understand the feature importance and feature selection with XGBoost in Python

Coding from scratch

• Understand and implement how to Load Machine Learning Data
• Learn to scale machine learning data
• Learn to execute simple Linear Regression
• Learn to execute the Perceptron Algorithm
• Learn to work on coding of Resampling Methods
• Learn to work on coding for Algorithm Performance Metrics
• Learn to work on coding of Backpropagation Algorithm
• Learn to work on coding in the Decision Tree Algorithm

Support Vector Machine (SVM)

• Understand the concepts of Support Vector Machine (SVM)
• Understand the Linear SVM Classification
• Understand Polynomial Kernel
• Understand the concept Radial Basis Function
• Define and describe Support Vector Regression

Deep learning using Keras

• Understand the Multi-Layer Perceptron Neural Networks
• Overview of convolutional Neural Networks for Machine Learning
• Introduction to Recurrent Neural Networks for Deep Learning
• Understand and learn the 5 Step Life-Cycle for Neural Network Models in Keras
• Learn to Grid Search Hyperparameters for Deep Learning Models in Python with Keras
• Learn to Save and Load Keras Deep Learning Models
• Understand the process of Dropout Regularization in Deep Learning Models with Keras
• Learn to use Convolutional Neural Networks in Python with Keras to recognize Handwritten Digits
• Learn to use Keras deep learning library for object recognition with Convolutional Neural Networks
• Learn to apply deep learning to predict sentiment from Movie Reviews
• Learn to work with Time Series Prediction with Long short-term memory Recurrent Neural Networks in Python with Keras
• Understand Stateful Long short-term memory Recurrent Neural Networks in Python with Keras
• Learn to generate text with Long short-term memory in recurrent Neural Networks in Python with Keras

Machine Learning Intermediate Certification

We provide a certificate of completion after you complete the course. Our trainers are experienced and certified to teach intermediate-level machine learning concepts. We will allow you to work on proof of concept. Our team of experts will verify the results from the POC to test your subject knowledge. If you are successful in getting satisfactory results, you will be given a certificate of completion. Don’t be disappointed if you are not able to meet the standards, and our expert faculty will guide you again to re-attempt the project. You will also be given a quiz to test your theoretical knowledge. On successful completion of the quiz and the POC, you will be given a certificate of completion of the course. The trainers are available 24/7 to clarify your doubts. Our certification endorses your skills and knowledge of machine learning skills. Our certification is acceptable in many organizations, as we are their learning partners. We will provide real-time case studies and projects for you to assimilate and apply the concepts learned. The practical exposure will get you an edge over your peers for job opportunities. You will gain employability skills and be a live wire in the job market.

Jobs and placement

Machine learning has become the brain behind business intelligence. Organizations are in dire need to hire machine learning professionals to solve their business problems. Every sector needs problem solvers. Therefore, there is a huge demand for machine learning experts and professionals. Machine learning can be implemented in chatbots, spam filtering, fraud detection, search engines, etc. The applications of machine learning are endless and in many industries.

According to the Gartner report, Machine learning and artificial intelligence will create 2.3 million jobs by 2020.  There will be more funding from CIO offices by 2020. A report from TMR mentions that MLaaS (Machine learning as a Service) is expected to grow from $1.07 billion to $19.9 billion by the end of 2025. The salary range for machine learning professionals is $98,000- $156,000. You may get placed as a machine learning researcher, machine learning engineer, data scientist, applied machine learning engineer, etc. You may get placed in companies like IBM, Deloitte, Microsoft, Spotify, Amazon, Visa, Volkswagen, Google, American Express, Facebook, Bosch, etc.