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Machine Learning Fundamentals In Depth in Bangalore

A comprehensive course on Machine Learning and AI using Python. This course is highly suited for beginners

12 weeks long course | 100% Online

Learn from leading experts in the industry

Project based learning with 2 industry level projects that learners can showcase on LinkedIn.

Learn Key Tools & Technologies Python

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Syllabus

This course is full of best-in-class content by leading faculty and industry experts in the form of videos and projects

Course Overview

  • This program is an extension of the machine learning course
  • Understanding of Mathematics behind different machine learning algorithms such as linear regression, principal component analysis etc
  • This is course is very important if someone wants to learn deep learning
  • This course also involves lot of Mathematics with pen and paper

Course Syllabus in Bangalore

On a daily basis we talk to companies in the likes of Tata Elxsi and Mahindra to fine tune our curriculum.

Week 01 - Basics of Probability and Statistics

  • In this module, we will introduce the ideas of Statistics, machine learning and artificial intelligence.
    • Basics of Probability
    • Basics of Statistics
    • What is ML & AI

Week 02 - Basics of Machine Learning (ML) & Artificial intelligence(AI)

  • In this section, we start with supervised learning.
    • Introduction to Normal Distribution & Standard Normal Distribution An introduction
    • to business moments
    • Artificial Intelligence

Week 03 - Supervised Learning - Prediction

  • In this section, we start with supervised learning.
    • Introduction to supervised learning
    • What is linear regression?
    • One hot encoding
    • Cost function and gradient descent

Week 04 - Supervised Learning - Classification

  • In this section, we start with a classification algorithm
    • Introduction to classification problems
    • What is logistic regression?
    • Cost function and gradient descent

Week 05 - Supervised Learning - Classification

  • In this module, we will introduce some more classification algorithms
    • Decision tree
    • Entropy
    • Information gain

Week 06 - Random forest & Model Evaluation

  • In this section, we will assess the algorithms
    • Random Forest
    • Bootstrapping and majority rule
    • Evaluation of classifiers

Week 07- Supervised Learning - Classification

  • In this module, we will introduce SVM
    • Support Vector Machines
    • Mathematical intuition behind SVM
    • How SVM is different from other classifiers

Week 08 - Supervised Learning - Classification

  • In this section, we will introduce knn
    • K-Nearest Neighbour
    • Lazy Algorithm
    • Single-layer Neural Network

Week 09 - Unsupervised Learning - Kmeans

  • In this section, we will introduce clustering
    • What is clustering
    • Why clustering is important
    • Kmeans and elbow curve

Week 10 - Unsupervised Learning - Hierarchical

  • In this section, we will introduce another type of clustering.
    • Hierarchical Clustering
    • Dendrogram
    • Evaluation of clustering algorithms

Week 11 - Unsupervised Learning - PCA

  • In this module, we will introduce some feature selection techniques.
    • Feature Selection
    • Principal Component Analysis
    • Mathematical intuition behind PCA

Week 12 - Supervised Learning - Classification

  • In this section, we will introduce neural networks.
    • An Artificial Neural Network
    • Deep learning
    • Different activation functions
    • Understanding back propagation

Our courses have been designed by industry experts to help students achieve their dream careers

Industry Projects in Bangalore

Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are 2 Projects that are available in this program.

Descriptive Analysis and Regression Analysis

In this project, the students will be working on the Descriptive Analysis and Regression Analysis based on the given input.

K-means Clustering

In this project, the students will be working on the K-means clustering algorithm based on the given input data.

Our courses have been designed by industry experts to help students achieve their dream careers

Ratings & Reviews by Learners

Skill-Lync has received honest feedback from our learners around the globe.

Google Rating
4.6

Excel in your career with our Machine Learning Fundamentals In-Depth course

The main objective of this Machine Learning Fundamentals In-Depth course is to teach you from basics to advanced concepts. Expertise in Machine Learning and Artificial Intelligence will help you secure lucrative career opportunities. Knowledge of Machine Learning and Artificial Intelligence will help you predict customer behaviour patterns. This could make you a key decision-maker in organisations.

This AI & ML course will cover both supervised and unsupervised learning in Machine Learning. You will learn about the basics of statistics and probability, which form the base of the subject. You will also get in-depth knowledge of what is AI & ML through this course.

Skill-Lync’s Machine Learning Fundamentals In Depth is a 12-week course. The fee structure for this AI ML course online is flexible. You can choose a plan from INR 7,000 to INR 15,000 as per your requirements.

Who Should Take This Course?

Industrial experts have designed this course for students and graduates from Computer Science and Information Technology backgrounds. This AI and ML course is highly suited for beginners who want to start from the basics.

Whoever is interested in building Machine Learning and Artificial Intelligence systems can take this course. As one of the best AI ML courses, this course is curated as per industry standards.

If you are looking for AI ML courses online to upskill yourself with the ongoing trends in technologies, this course will suit you well.

What Will You Learn in This Course?

Through this Machine Learning Fundamentals In-Depth, you will learn industry-relevant concepts in this AI & ML course. In the first week, you will be introduced to basic concepts of statistics and probability needed for a good understanding of AI and ML concepts. As you progress through, you will cover the basics of ML and Al and business moments. Along with that, you will also learn what is AI & ML.

You will start learning supervised and unsupervised learning. You will explore the Random forest, Bootstrapping, and majority rule concepts. Key concepts include logistic regression, which is used for prediction analysis and cost function and gradient descent will be covered. Some mathematics concepts applied in Support Vector Machines will be covered in the curriculum. Concepts of clustering and clustering algorithms, and you will understand what is Deep Learning through this AI ML course. In this course, you will work on descriptive analysis and regression analysis, and K-means clustering, which are industry projects.

Skills You Will Gain

  • By working on real-time projects, you will become industry-ready.
  • A sound understanding of concepts involved in AI and ML.
  • You will gain skills in Deep Learning which is a key technology for many applications.
  • Also, a knowledge of mathematics will enhance your analytical skills.

Key Highlights of the Program

  • The duration of the course is 12 weeks.
  • Besides the course completion certificate for all participants, the top 5% of learners get a merit certificate.
  • You will get Individual Video Support, Group Video Support, Email Support, and Forum Support to clear your queries and doubts.
  • Real-time industry-relevant projects will make your learning purposeful.

Career Opportunities after Taking This Course

Taking this course would open up a plethora of career opportunities. According to AmbitionBox, the average salary of a Machine learning engineer is 7.5 LPA. With knowledge and skill gained through this AI ML course online, you can earn like other ML Engineers. Some positions that you can work for include,

  • Machine Learning Engineer: The role of an ML Engineer is to develop and train AI software and Machine Learning systems.
  • Data Scientists: Data Scientists work over structured and unstructured data to convert it into key actionable insights for organisations.
  • AI Engineer: The role of the AI Engineer is to build AI models that would help enterprises understand results better.

FAQs on Machine Learning Fundamentals In Depth

  1. Who can take up AI ML courses?

Students and graduates with a technical background in Computer Science can opt for this course.

  1. Is this an online AI & ML course?

Yes, this is a 100% online course.

  1. What is AI & ML?

Artificial Intelligence is involved in making systems that can work like humans. Machine Learning is a subset of AI where the machine is allowed to learn on its own from provided data sets.

  1. What is the fee for this AI ML course?

The fee structure is flexible, and you can choose a plan that suits you. The basic plan would give you two months of access, the pro plan would give you four months of access, and the premium plan would provide you with lifetime access.

  1. How much can a Machine Learning Engineer earn?

According to AmbitionBox, the average salary of a Machine learning engineer is 7.5 LPA. After completing this course, you can earn this average pay and expect more once you become an experienced professional.

  1. Is there any certificate for completing this Machine Learning Fundamentals In-Depth course?

Yes, after completing this best AI ML course, you shall be given a course completion certificate. The top 5% of the scorers shall be given a merit certificate alongside the course completion certificate.

  1. Is there any technical support for this AI ML course to clear doubts?

Yes, you can clear your queries with email and forum support.

Instructors profiles

Our courses are designed by leading academicians and experienced industry professionals.

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1 industry expert

Our instructors are industry experts along with a passion to teach.

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8 years in the experience range

Instructors with 8 years extensive industry experience.

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Areas of expertise

  • Machine Learning
  • Deep Learning

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