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Machine Learning Training & Certification Program
Post Graduate Program In AI And Machine Learning Course. Online and Offline Training
This course Learns how to deploy machine learning models using cloud computing with an advanced certification program. Our Certified Instructors learn this exciting branch of Artificial Intelligence with a program consisting of 60 hours of applied learning, interactive labs, 5 hands-on projects and mentoring.
This machine learning online training will give you the skills you need to become a successful machine learning engineer today.
💡 One among the top 10 most valuable certification
💡 90% of the people get job after Certification
💡 Experienced and Certified Trainers
About Program
Our Certified Trainers offers an in-depth overview of Machine Learning topics including Data Science working with real-time data, developing algorithms using supervised & unsupervised learning, regression, classification, and time series modeling in Machine Learning Courses. Learn how to use machine learning techniques by analyzing data and making predictions based on it.
Benefits
- Gain expertise with 25+ hands-on exercises
- 20+ Case Studies and Projects
- Dedicated mentoring sessions from industry experts
- No Cost EMI Option
- Practical Hands-on Workshops
- Industry Readiness Assessments
Top Skills You Will Learn
- Python
- BD processing using Spark
- Deploy ML Models
- Supervised & Unsupervised ML Models
- Predictive Analytics & Statistics
Opportunities
- Big Data Analyst
- Data Engineer
- Data Scientist
- Machine Learning Engineer
- Machine Learning Engineer
- Decision scientist
Machine Learning Course Overview
The Machine Learning market is expected to reach USD $30.64 Billion by 2024, at a Compound Annual growth rate (CAGR) of 42.8-percent, indicating the increased adoption of Machine Learning among companies. By 2024, the demand for Machine Learning engineers is expected to grow by 11-percent.
Every industry makes data-driven decisions to reach their potential consumers/audiences to improve their business. Data Science uses machine learning techniques to analyze data and make predictions based on it. For example, you can use machine learning techniques to predict different sales for a business at more than one location.
In Data Science, we collect large chunks of data, filter/group them based on our need/application, and use math and statistics to see the patterns behind it. That way, we get the answers to the questions we need. Whereas, in machine learning, we use data for testing and training our models. It is all about building models that can learn from the given data and provide results or predictions based on recent trends.
Examples: recognizing fingerprints, estimating stock prices, and self-driving cars.
Course Curriculum
- Python Programming
- Python for Data Science
- Installing Python
- Data Visualisation
- Python Assignment (Optional)
- Exploratory Data Analysis and Data Visualisation
- Intro to ML + Understanding the ML pipeline
- Introduction to Cloud
- Elastic Provision Services of Cloud and Setting up Cloud
- Assignment
- Introduction to HDFS
- Introduction to SQL
- Hive Querying
- Case Study
- Assignment
- Introduction to Spark
- PySpark Programming
- PySpark implementation on a Dataset: ALS & Spark Streaming
- Assignment
- Inferential Statistics and Hypothesis Testing
- Linear Regression - I
- Linear Regression - II
- Logistic Regression
- Assignment
- Decision Trees
- Random Forest Trees
- Assignment
- Principal Component Analysis + Linear Algebra
- Clustering
- Introduction to Neural Nets with TensorFlow
- Convolution Neural Networks
- Melanoma Detection Assignment
- Recurrent Neural Networks
- Natural Language Processing Fundamentals
- Case Study on applications of Deep Learning in NLP
- Case Study on Computer Vision
- NN Assignment: Gesture Recognition
- Deployment
- Implementation: Regression + Model Selection on SageMaker
- Capstone Project