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ABOUT MASTER IN MACHINE LEARNING COURSE
An exciting branch of Artificial Intelligence, this Machine Learning certification online course will provide the skills you need to become a Machine Learning Engineer and unlock the power of this emerging field. Our Machine Learning course will help you master the skills required to become an expert in this domain. Master skills such as Python, ML algorithms, statistics, supervised and unsupervised learning, etc. to become a successful professional in this popular technology. Our Machine Learning certification training comes with 24/7 support, multiple assignments, and project work to help you gain real-world exposure. If you are willing to become a master in ML then let's explore the concepts of Machine Learning and understand how it’s transforming the digital world.
MASTER IN MACHINE LEARNING COURSE OVERVIEW
We offer an in-depth overview of Machine Learning topics including working with real-time data, developing algorithms using supervised & unsupervised learning, regression, classification, and time series modeling in our Machine Learning online course.
- Acquire expertise with 25+ hands-on exercises
- Real-life industry projects with integrated labs
- Assigned & exclusive mentoring sessions from industry experts
- Instructor-led training with certification
Advantage:-
Market value of machine learning domain is to be expected to reach about $8.81 Billion by 2022, as because its growth rate is 44.1-percent. The demand for machine learning engineers is going to be grown by 60% which is indicated by the increased adoption of Machine learning among companies.
Instructor-led, Live & Interactive Sessions
Duration |
Mode |
Agenda |
|---|---|---|
48 Hours |
Online (Instructor-led) |
Machine Learning |
Course Price at
49,999/-
59,999
[Fixed - No Negotiations]
How we prepare you
Machine Learning
Upon completion of this program you will get 360-degree understanding of Machine Learning. This course will give you thorough learning experience in terms of understanding the concepts, mastering them thoroughly and applying them in real work environment.
Hands-on experience in a live project
You will be given industry level real time projects to work on and it will help you to differentiate yourself with multi-platform fluency, and have real-world experience with the most important tools and platforms.
Unlimited Mock Interview and Quiz
As part of this, You would be given complete interview preparations kit, set to be ready for the Machine Learning. This kit has been crafted by 200+ years industry experience and the experiences of nearly 10000 DevOpsSchool's Machine Learning learners USA.
Agenda of the Master in Machine Learning CourseDownload Curriculum
- Introduction to Machine Learning(ML)
- Linear Regression & Supervised Learning
- Logistic Regression & Classification
- Decision Tree and Random Forest
- Support Vector Machine (self-paced) & Naïve Bayes
- Text Mining (self-paced) & Natural Language Processing
- Deep Learning Introduction
- Time Series Analysis (self-paced)
- Requirement of Machine Learning
- Well Introduction to Machine Learning
- Types of Machine Learning, such as supervised, unsupervised, and reinforcement learning,
- Machine Learning with Python, and the other applications of Machine Learning(ML)
- Will be Introduced to supervised learning and the other types of supervised learning, like regression and classification
- Will be Introduced to regression
- Simple linear regression
- Multiple linear regression and assumptions in linear regression
- Math behind linear regression
- Implementation of linear regression from scratch in Python
- Using Python library Scikit-Learn to perform simple linear regression and multiple linear regression
- Implementation of train–test split and predicting the values on the test set
- Introduction to classification
- Linear regression vs logistic regression(Difference)
- Math behind logistic regression, detailed formulas, the logit function and odds, confusion matrix and accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR
- Implementation of logistic regression from scratch with Python
- Using Python library Scikit-Learn to perform simple logistic regression and multiple logistic regression
- Building a confusion matrix to find out accuracy, true positive rate, and false positive rate.
- Introduction to tree-based classification
- Understanding a decision tree, impurity function, entropy, and understanding the concept of information gain for the right split of node
- Understanding the concepts of information gain, impurity function, Gini index, overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning
- Introduction to ensemble techniques, bagging, and random forests and finding out the right number of trees required in a random forest
- Implementing a decision tree from scratch in Python
- Using Python library Scikit-Learn to build a decision tree and a random forest
- Visualizing the tree and changing the hyper-parameters in the random forest
- Introduction to probabilistic classifiers
- Understanding Naïve Bayes and math behind the Bayes theorem
- Understanding a support vector machine (SVM)
- Kernel functions in SVM and math behind SVM
- Using Python library Scikit-Learn to build a Naïve Bayes classifier and a support vector classifier
- Types of unsupervised learning, such as clustering and dimensionality reduction, and the types of clustering
- Introduction to k-means clustering
- Math behind k-means
- Dimensionality reduction with PCA
- Using Python library Scikit-Learn to implement k-means clustering
- Implementing PCA (principal component analysis) on top of a dataset
- Introduction to Natural Language Processing (NLP)
- Introduction to text mining
- Importance and applications of text mining
- How NPL works with text mining
- Writing and reading to word files
- Language Toolkit (NLTK) environment
- Text mining: Its cleaning, pre-processing, and text classification
- Learning Natural Language Toolkit and NLTK Corpora
- Reading and writing .txt files from/to a local drive
- Reading and writing .docx files from/to a local drive
- Introduction to Deep Learning with neural networks
- Biological neural networks vs artificial neural networks
- Understand the perception learning algorithm, introduction to Deep Learning frameworks, and TensorFlow constants, variables, and place-holders
- What is a time series? Its techniques and applications
- Time series components
- Moving average, smoothing techniques, and exponential smoothing
- Univariate time series models
- Multivariate time series analysis
- ARIMA model and time series in Python
- Sentiment analysis in Python (Twitter sentiment analysis) and text analysis
- Analyzing time series data
- The sequence of measurements that follow a non-random order to recognize the nature of the phenomenon
- Forecasting the future values in the series
PROJECT
In Machine Learning Course a Participant will get total 5 real time scenario based projects to work on, as part of these projects, we would help our participant to have first hand experience of real time scenario based software project development planning, coding, deployment, setup and monitoring in production from scratch to end. We would also help our participants to visualize a real development environment, testing environment and production environments.
Interview
As part of this, You would be given complete interview preparations kit, set to be ready for the Machine Learning hotseat. This kit has been crafted by 200+ years industry experience and the experiences of nearly 10000 DevOpsSchool Machine Learning learners USA.
OUR COURSE IN COMPARISON
| FEATURES | DEVOPSSCHOOL | OTHERS |
|---|---|---|
| 1 Course for Machine Learning | ||
| Faculty Profile Check | ||
| Lifetime Technical Support | ||
| Lifetime LMS access | ||
| Step by Step Web Based Tutorials | ||
| Training Slides | ||
| Training + Additional Videos |
Requirement for this machine learning course is understanding of basic statistics and mathematics at the college level and familiar with python programing is also beneficial .One must understand the these fundamental courses including Python, for data science,Math refresher and statistic essential for data science before getting into machine learning
The online course of machine learning certification is very well suited for participants at the intermediate level including analytics manager, information architects, developers who are looking forward to becoming a data scientist , and graduates seeking a bright career in data science and machine learning.
