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Machine Learning Fundamentals In Depth
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
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
More and more industries are relying on machine learning to develop systems that function on their own through repetitive learning. As machine learning is widespread across a variety of domains and industries, it has become a standard prerequisite for engineers striving to work in the industry. This course will offer aspiring engineers a solid understanding of the fundamental machine learning algorithms.
You will learn about...
- Basic probability and statistics essential for understanding ML and AI
- Supervised learning (Prediction, classification)
- Random Forest and Model Evaluation
- Unsupervised Learning (K Means, Hierarchical, PCA)
In addition to the theory taught in this course, you will get hands-on exposure to the use of Python for machine learning applications. This course culminates with industry level projects focused on real-time application of machine learning concepts.
Who can enrol in this course?
Engineering graduates and working professionals with a background in similar fields can take this course.
Course Syllabus
On a daily basis we talk to companies in the likes of Tata Elxsi and Mahindra to fine tune our curriculum.
Week 1 - Basics of Probability and Statistics
This week, you will learn about
- Basics of Probability
- Basics of Statistics
- What ML & AI is
Week 2 - Basics of Machine Learning (ML) & Artificial intelligence (AI)
This week, you will learn about
- Normal Distribution & Standard Normal Distribution: Introduction
- Business Moments: Introduction
- Artificial Intelligence
Week 3 - Supervised Learning - Prediction
This week, you will learn about
- Supervised learning: Introduction
- What linear regression is
- One hot encoding
- Cost function and gradient descent
Week 4 - Supervised Learning - Classification
This week, you will learn about
- Classification problems: Introduction
- What logistic regression is
- Cost function and gradient descent
Week 5 - Supervised Learning - Classification
This week, you will learn about
- Decision tree
- Entropy
- Information gain
Week 6 - Random Forest & Model Evaluation
This week, you will learn about
- Random forest
- Bootstrapping and majority rule
- Evaluation of classifiers
Week 7- Supervised Learning - Classification
This week, you will learn about
- Support Vector Machines (SVM)
- Mathematical intuition behind SVM
- How SVM is different from other classifiers
Week 8 - Supervised Learning - Classification
This week, you will learn about
- K-Nearest Neighbor
- Lazy Algorithm
- Single-layer Neural Network
Week 9 - Unsupervised Learning - K-Means
This week, you will learn about
- What clustering is
- Why clustering is important
- K Means and elbow curve
Week 10 - Unsupervised Learning - Hierarchical
This week, you will learn about
- Hierarchical Clustering
- Dendrogram
- Evaluation of clustering algorithms
Week 11 - Unsupervised Learning - PCA
This week, you will learn about
- Feature Selection
- Principal Component Analysis (PCA)
- Mathematical intuition behind PCA
Week 12 - Supervised Learning - Classification
This week, you will learn about
- Artificial Neural Networks
- 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
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, you will perform descriptive analysis on given data regarding cars. Using the data, you will have to classify cars based on various parameters and analyze the pricing trends. You will then have to use Python to read the data and identify parameters on which data cleaning has to be performed. In addition to this, you will create a correlation matrix to identify predominant characteristics and then perform regression analysis on them.
K-Means Clustering
In this project, you will have to clean a data set regarding cars and implement the clustering algorithm using K-Means. You will then have to explain the results and find the optimum values using the Elbow curve.
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.
Instructors profiles
Our courses are designed by leading academicians and experienced industry professionals.
1 industry expert
Our instructors are industry experts along with a passion to teach.
8 years in the experience range
Instructors with 8 years extensive industry experience.
Areas of expertise
- Machine Learning
- Deep Learning
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