Machine Learning Master Course
About Course
Summary
This online Machine Learning course by Coding Blocks is one of its kind. The course comprising of 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 optimising solutions to the real-world problems. It is just a matter of weeks before the students actually 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 so as to make them grasp the concepts of Machine Learning quickly and effortlessly.
Course Highlights
The course is broadly divided in 7 categories, and covers many topics from Coding Blocks Data Science & Deep Learning Course.
• Introduction to Machine Learning
• Supervised Learning Algorithms
• Unsupervised Learning
• Deep Learning
• Deep Learning in Computer Vision
• Deep Learning Natural Language Processing
• Reinforcement Learning
Course Content
Course Repository
Quickstart Mode
Python 01 – Basics
Python 02 – Functions
Python 03 – Builtin Data Structures
Python 04 – Object Oriented Programming & Modules
Python 05 – File and Error Handling
Python Practice Problems
Intermediate Python
Asynchronous Programming in Python (Optional)
Machine Learning Introduction
Numpy
Linear Algebra
Data Visualisation
Seaborn
Pandas
Project – Movie Recommendation System
Probability Distribution & Statistics
K-Nearest Neighbours
Diabetes Detection Challenge
Project – Real Time Face Recognition using KNN
Challenge – Make Snapchat like Filters
Linear Regression
Regression Challenge – Hardwork Pays Off
Linear Regression – II Multiple Features
Sci-kit Learn Introduction
Air Quality Prediction Challenge
Optimisation Algorithms
Locally Weighted Regression (LOWESS)
Maximum Likelihood Estimate (MLE) [Proof]
Logistic Regression
Separating Chemicals Challenge
Feature Selection
PCA
Text Preprocessing (NLP Basics)
Naive Bayes Classifier
Naive Bayes & NLP Challenge
Project – Movie Rating Prediction Challenge
K-Means Clustering (Unsupervised)
Project – Image Segmentation using K-Means
Challenge – K-Means
SVM – Support Vector Machines
Project – Image Classification using SVM
Classify Pokemon’s using SVM Challenge
Decision Trees & Random Forests
Predicting Titanic Survivors Challenge
Perceptrons & Neural Networks
Artificial Neural Networks
Project – Image Classsification using Neural Network
Project – Sentiment Analysis using Neural Networks
Neural Networks Challenges
Convolutional Neural Networks
Digging Deeper into Convnets
Data Loaders, Augmentation, Colab
CNN Case Studies
Project – Covid Detection using CNN
Transfer Learning in Vision
Building Mobile Friendly Network Challenge
Markov Chains for Text Generation (NLP)
Lyric Generation Challenge
Word Embeddings
Odd One Challenge
Recurrent Neural Networks
Project – Emoji Prediction using Transfer Learning (NLP)
RNN Challenges
Reinforcement Learning for Games
Challenge – Mountain Car RL Challenge
Generative Adversarial Networks
Deep Convolutional GAN’s (DCGANs)
Machine Learning Challenges
Quizzes
CAPSTONE PROJECT – AI Image Caption Bot
Integrating ML Models with Web
Introduction to PyTorch
Tensorflow Introduction
Project Ideas
Student Ratings & Reviews
