Machine Learning Fundamentals

Categories: machine learning
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About Course

Summary

This online Machine Learning course by Coding Blocks is one of its kind. The course comprising over 200 recorded tutorials and 20 mini projects for teaching, boasts of an all-exhaustive and highly comprehensive curriculum. The Machine Learning online course starts with the essentials of Python, gradually moving towards to concepts of advanced algorithms and finally into the cores of Machine Learning. With our key focus being the live projects, we dive deeper into the fundamentals of classical algorithms and deep neural networks enabling the students to work out optimized solutions to the real-world problems. It is just a matter of weeks before the students begin building intelligent systems, working on AI algorithms, and data crunching. As a part of these online Machine Learning classes, a detailed overview of the programming fundamentals and Python Basics would be covered with the students to make them grasp the concepts of Machine Learning quickly and effortlessly.

Course Highlights

  • • Introduction to Machine Learning
  • • Supervised Learning Algorithms
  • • Unsupervised Learning
  • • Deep Learning Introduction
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What Will You Learn?

  • Introduction to Machine Learning Concepts
  • Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
  • Data Preparation and Feature Engineering
  • Understanding and Implementing Linear Regression
  • Classification Algorithms (e.g., Decision Trees, K-Nearest Neighbors)
  • Model Evaluation and Validation Techniques
  • Overfitting and Regularization
  • Clustering Algorithms (e.g., K-Means, Hierarchical Clustering)
  • Introduction to Neural Networks and Deep Learning
  • Hands-on Projects and Real-World Applications

Course Content

Machine Learning Introduction
5 Items | Duration : 55mins

Quickstart Mode
7 Items | Duration : 1hrs

Project – Movie Recommendation System
4 Items | Duration : 59mins

Linear Regression
11 Items | Duration : 2hrs

Regression Challenge – Hardwork Pays Off
1 Items | Duration : 1hrs

Linear Regression – II Multiple Features
4 Items | Duration : 1hrs

Sci-kit Learn Introduction
2 Items | Duration : 11mins

Air Quality Prediction Challenge
1 Items | Duration : 1hrs

Optimisation Algorithms
3 Items | Duration : 35mins

Locally Weighted Regression (LOWESS)
9 Items | Duration : 1hrs

Maximum Likelihood Estimate (MLE) [Proof]
2 Items | Duration : 39mins

Logistic Regression
12 Items | Duration : 2hrs

Separating Chemicals Challenge
2 Items | Duration : 1hrs

K-Nearest Neighbours
3 Items | Duration : 33mins

Diabetes Detection Challenge
2 Items | Duration : 1hrs

Project – Real Time Face Recognition using KNN
8 Items | Duration : 1hrs

Challenge – Make Snapchat like Filters
1 Items | Duration : 1hrs

Feature Selection
6 Items | Duration : 1hrs

PCA
6 Items | Duration : 1hrs

Text Preprocessing (NLP Basics)
10 Items | Duration : 1hrs

Naive Bayes Classifier
17 Items | Duration : 3hrs

Project – Movie Rating Prediction Challenge
8 Items | Duration : 1hrs

Naive Bayes & NLP Challenge
3 Items | Duration : 1hrs

K-Means Clustering (Unsupervised)
7 Items | Duration : 1hrs

Project – Image Segmentation using K-Means
3 Items | Duration : 24mins

Challenge – K-Means
1 Items | Duration : 15mins

SVM – Support Vector Machines
13 Items | Duration : 3hrs

Project – Image Classification using SVM
6 Items | Duration : 1hrs

Classify Pokemon’s using SVM Challenge
2 Items | Duration : 1hrs

Decision Trees & Random Forests
10 Items | Duration : 3hrs

Ensemble Learning (Coming Soon)
Coming Soon!

Predicting Titanic Survivors Challenge
2 Items | Duration : 1hrs

Perceptrons & Neural Networks
10 Items | Duration : 2hrs

Artificial Neural Networks
16 Items | Duration : 3hrs

Project – Image Classsification using Neural Network
3 Items | Duration : 39mins

Project – Sentiment Analysis using Neural Networks
3 Items | Duration : 42mins

Neural Networks Challenges
4 Items | Duration : 2hrs

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