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Machine Learning & Deep Learning in 30hrs
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
Machine Learning & Deep Learning in 31.5hrs online certifications is developed by Mario Favaits - Certified AI Professional & Trainer and is offered by Udemy which is designed for those who want to learn the fundamentals of machine learning and deep learning to become certified data scientists. The Machine Learning & Deep Learning in 30hrs online course by Udemy takes a practical approach, using a logical sequence to help participants understand data science and machine learning principles effectively.
As no prior knowledge of coding is required, the Machine Learning & Deep Learning in 30hrs online classes begin with the fundamentals of linear algebra, calculus, and statistics. This course aims to give students a thorough understanding of topics such as artificial intelligence, machine learning, unsupervised learning, neural networks, computer vision, data augmentation, backpropagation, and random forest, as well as various data science tools such as Python, Pandas, Keras, and Colab.
The highlights
- Certificate of completion
- Self-paced course
- 31.5 hours of pre-recorded video content
- 70 downloadable resources
- Learning resources
Program offerings
- Online course
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and tv
Course and certificate fees
Fees information
₹ 799
certificate availability
Yes
certificate providing authority
Udemy
Who it is for
What you will learn
Machine learning
Data science knowledge
Knowledge of deep learning
After completing the Machine Learning & Deep Learning in 30hrs certification course, participants will obtain a practical comprehension of data science, machine learning, deep learning, and artificial intelligence principles and methodologies. Participants will explore the functionalities of neural networks, convolutional neural networks, recurrent neural networks, and unsupervised learning. Participants will learn about data augmentation, computer vision, regression, and various tools like Python, Pandas, Keras, and Colab. Participants will also learn about backpropagation, XGBoost, GAN, LSTN, SVM, KNN, GRU, Naive Bayes, random forest, and more.
The syllabus
Course Introduction - 6 videos
Definitions, Fundamentals and History of AI - 5 videos
- What is AI, what is Machine Learning and what is Deep Learning?
- History of AI
- Regression Problem & Cost Function
- Classification
- Supervised, Unsupervised and Reinforcement Learning
Linear Algebra and Calculus Refresher - 11 Videos
- Scalars and Vectors 1
- Scalars and Vectors 2
- Matrices 1
- Matrices 2
- Eigenvalues and Eigenvectors
- Tensors
- Derivatives
- Convex Functions
- Partial Derivatives
- Lagrange Multipliers
- Taylor Series
Gradient Descent and Optimizers - 10 videos
- Gradient Descent 1
- Gradient Descent 2
- Multi-Variate Gradient Descent
- Contour Plots
- Newton's Method
- Stochastic Gradient Descent
- Optimizers | Adagrad | RMSprop
- Optimizers | Momentum | Adam
- Feature Scaling | Feature Normalisation
- Gradient Descent Summary
Classification and Classification Performance Indicators - 4 videos
- Classification - Introduction
- Confusion Matrix
- ROC Curve | AUC
- F1 Score
K nearest neighbours (KNN) - 8 Videos
- Introduction to KNN
- How to select K?
- Curse of Dimentionality
- Coding Brief
- Coding 1/4
- Coding 2/4
- Coding 3/4
- Coding 4/4
Dimensionality Reduction - 7 Videos
- Introduction
- Principal Component Analisys (PCA) 1
- Principal Component Analisys (PCA) 2
- Principal Component Analisys (PCA) 3
- Singular Value Decomposition (SVD) 1
- Singular Value Decomposition (SVD) 2
- Coding
Statistics Refresher - 13 Videos
- Introduction
- Random Variable & Probability Distribution
- Bayes' Rule
- MLE and MAP
- Expected Value and Variance of a Random Variable
- Bernoulli Distribution
- Binomial Distribution 1
- Binomial Distribution 2
- Gaussian Distribution 1
- Gaussian Distribution 2
- Weak Law of Large Numbers (WLLN)
- Central Limit Theorem (CLT)
- Covariance and Variance Matrix
The Perceptron - 6 videos
- The Perceptron 1
- The Perceptron 2
- XOR Problem
- Activation Function
- SoftMax and ReLu
- Summary
Naive Bayes - 10 Videos
- Introduction
- Naive Bayes 1
- Naive Bayes 2
- Naive Bayes - Continuous Case
- Naive Bayes - Discreet Case 1
- Naive Bayes - Discreet Case 2
- Coding Brief
- Coding 1/3
- Coding 2/3
- Coding 3/3
Logistic Regression - 14 Videos
- Introduction
- Logistic Regression - 1
- Normal Equation
- Logistic Regression - 2
- Logistic Regression - 3
- Logistic Regression - 4
- Logistic Regression - 5
- Logistic Regression - 6
- Logistic Regression - 7
- Coding Brief
- Coding 1/3
- Coding 2/3
- Coding 3/3
- Coding Results
Support Vector Machines - 12 Videos
- Introduction
- SVM 1
- SVM 2
- SVM 3
- SVM 4
- Kernel Trick
- Coding Brief
- Coding Brief - Hyperparameter Tuning
- Coding 1/4
- Coding 2/4
- Coding 3/4
- Coding 4/4 + Results
Underfitting, Overfitting & Regularizaion - 5 Videos
- Introduction
- Bias Variance Decomposition
- Regularization 1
- Regularization 2
- Regularization 3
Decision Trees - 28 Videos
- Classifiers: Taking Stock
- KD Trees 1
- KD Trees 2
- Information Content
- Entropy
- Cross-Entropy 1
- Cross-Entropy 2
- KL Divergence
- Entropy over a Tree
- Bagging
- Random Forest
- Random Forest: Coding
- Boosting
- ADABoost 1
- ADABoost 2
- Gradient Boosting 1
- Gradient Boosting 2
- Gradient Boosting 3
- Gradient Boosting 4
- Gradient Boosting 5
- Gradient Boosting vs. Gradient Descent
- Coding Brief 1
- Coding Brief 2: Hyperparameter Tuning: GridSearch & Random Search
- Coding Brief 3: Hyperparameter Tuning: Bayesian Optimization
- Coding Brief 4
- Coding 1/3
- Coding 2/3
- Coding 3/3
Unsupervised Learning - Recommender Systems - 8 Videos
- Taking Stock
- Introduction to Recommender Systems
- Content Based Systems
- Collaborative Filtering 1
- Collaborative Filtering 2
- Collaborative Filtering 3
- Collaborative Filtering 4
- Collaborative Filtering 5
Unsupervised Learning - Anomaly Detection
- Unsupervised Learning & Anomaly Detection Introduction
- Anomaly Detection Systems: Simple Statistics & Density Based Methods
- Anomaly Detection Systems: Local Outlier Factor (LOF)
- Anomaly Detection Systems: One Class SVM
- Anomaly Detection Systems: Isolated Forest
- Anomaly Detection Systems: Coding Brief
- Coding 1/4
- Coding 2/4
- Coding 3/4
- Coding 4/4
- Coding Results
Unsupervised Learning - Clustering
- Introduction Clustering
- K-Means 1
- K-Means 2
- Coding Brief
- Coding 1/2
- Coding 2/2
Deep Learning - 15 Videos
- Introduction
- The Perceptron
- From Perceptron to Neural Networks
- Neural Networks Terminology
- Coding Brief
- Coding 1/1
- Backpropagation 1
- Backpropagation 2
- Coding Brief
- Coding 1/1
- Regularization
- Coding 1/3
- Coding 2/3
- Coding 3/3
- Results Brief
Convolutional Neural Networks - Computer Vision: 34 Videos
- Covnets Introduction 1
- Covnets Introduction 2
- Analog & Digital Signals
- Signal Theory - LTI Systems 1
- Signal Theory - LTI Systems 2
- Signal Theory - LTI Systems 3
- Signal Theory - LTI Systems 4
- Why do we need Covnets?
- Covnet Elements
- Covnet Architecture 1
- Covnet Architecture 2
- Coding Brief Covnets
- Coding - Data Upload
- Coding Brief Data Preparation 1
- Coding Brief Data Preparation 2
- Coding 1/2
- Coding 2/2
- Data Augmentation
- Coding 1/1
- Feature Extraction
- ResNet 1
- ResNet 2
- ResNet 3
- ResNet 4
- Transfer Learning - Feature Extraction
- Coding: Transfer Learning - ResNet50 1/3
- Coding: Transfer Learning - ResNet50 2/3
- Coding: Transfer Learning - ResNet50 3/3
- Coding: Transfer Learning - VGG16
- Transfer Learning - Results
- Transfer Learning - Finetuning
- Coding VGG16 - Finetuning
- Coding ResNet - Finetuning
- Coding Results Finetuning
Dealing with Text Data - 13 Videos
- Introduction
- From Words to Vectors
- T-SNE
- Word Embeddings CBOW 1
- Word Embeddings CBOW 2
- Word Embeddings CBOW 3
- Word Embeddings SKIP GRAM
- Negative Sampling 1
- Negative Sampling 2
- Hierarchical Softmax
- Direct co-occurence count
- Glove
- Batch Normalization
Recurrent Neural Networks - 11 Videos
- Introduction
- BPTT 1
- BPTT 2
- BPTT 3
- Selective Write, Read and Forget
- LSTM & GRU
- Coding Brief
- Coding Simple RNN
- Coding Transfer Learning - GloVe
- Coding LSTM
- Coding Results
RNNs & Time Series - 13 Videos
- Coding Brief 1
- Coding Brief 2
- Coding 1/3
- Coding 2/3
- Coding 3/3
- Recurrent Dropout & Stacking
- Coding Recurrent Dropout and Stacking
- Bidirectional RNNs
- Coding Bidirectional RNNs
- 1D Covnet
- Coding 1D Covnet
- Coding 1D Covnet + Recurrent Dropout
- Coding Results
- Sequence to Sequence Models
- Attention
- Transformers
Generative Deep Learning: Everybody an Artist
- Introduction
- Coding Brief
- Coding 1/3
- Coding 2/3
- Coding 3/3
- Neural Style Transfer 1
- Neural Style Transfer 2
- Variational Autoencoders (VAEs) 1
- Variational Autoencoders (VAEs) 2
- Variational Autoencoders (VAEs) 3
- Variational Autoencoders (VAEs) 4
- Variational Autoencoders (VAEs) 5
- Variational Autoencoders (VAEs) 6
- Variational Autoencoders (VAEs) 7
- GANs 1
- GANs 2
- GANs 3
- GANs 4
- GANs 5
- GANs 6
Instructors
Mr Mario Favaits
Seasoned Executive with deep understanding of AI
Freelancer
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