About this course
Introduction :
1. Getting Started with Machine Learning
2. An Introduction to Machine Learning
3. What is Machine Learning ?
4. Introduction to Data in Machine Learning
5. Demystifying Machine Learning
6. ML – Applications
7. Best Python libraries for Machine Learning
8. ArtificialIntelligence | An Introduction
9. Machine Learning and ArtificialIntelligence
10. Difference between Machine learning and Artificial Intelligence
11. Agents in Artificial Intelligence
12. 10 Basic Machine Learning Interview Questions
Data and It’s Processing:
1. Introduction to Data in Machine Learning
2. Understanding Data Processing
3. Python | Create Test DataSets using Sklearn
4. Python | Generate test datasets for Machine learning
5. Python | Data Preprocessing in Python
6. Data Cleaning
7. FeatureScaling – Part 1
8. FeatureScaling – Part 2
9. Python | Label Encoding of datasets
10. Python | One Hot Encoding of datasets
11. Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python
12. Dummy variable trap in Regression Models
Supervised learning :
1. Getting started with Classification
2. Basic Concept of Classification
3. Types of Regression Techniques
4. Classification vs Regression
5. ML | Types of Learning – Supervised Learning
6. Multiclass classification using scikit-learn
7. Gradient Descent :
• Gradient Descent algorithm and its variants
• Stochastic Gradient Descent(SGD)
• Mini-Batch Gradient Descent with Python
• Optimization techniques for Gradient Descent
• Introduction to Momentum-based Gradient Optimizer
8. Linear Regression :
• Introduction to Linear Regression
• Gradient Descent in Linear Regression
• Mathematical explanation for Linear Regression working
• Normal Equation in Linear Regression
• Linear Regression (Python Implementation)
• Simple Linear-Regression using R
• Univariate Linear Regression in Python
• Multiple LinearRegression using Python
• Multiple LinearRegression using R
• Locally weighted Linear Regression
• Generalized Linear Models
• Python | Linear Regression using sklearn
• Linear RegressionUsing Tensorflow
• A Practical approach to Simple Linear Regression using R
• Linear Regression using PyTorch
• Pyspark | Linear regression using Apache MLlib
• ML | Boston Housing Kaggle Challenge with Linear Regression
9. Python | Implementation of Polynomial Regression
10. Softmax Regression using TensorFlow
11. Logistic Regression :
• Understanding Logistic Regression
• Why Logistic Regression in Classification ?
• Logistic Regression using Python
• Cost function in Logistic Regression
• Logistic Regression using Tensorflow
12. Naive Bayes Classifiers
13. Support Vector:
• Support Vector Machines(SVMs)in Python
• SVM Hyperparameter Tuning using GridSearchCV
• Support Vector Machines(SVMs)in R
• Using SVM to perform classification on a non-linear dataset
14. Decision Tree:
• Decision Tree
• Decision Tree Regression using sklearn
• Decision Tree Introductionwith example
• Decision tree implementation using Python
• Decision Tree in Software Engineering
15. Random Forest:
• Random Forest Regression in Python
• Ensemble Classifier
• Voting Classifier using Sklearn
• Bagging classifier
Unsupervised learning :
1. ML | Types of Learning – Unsupervised Learning
2. Supervised and Unsupervised learning
3. Clustering in Machine Learning
4. Different Types of Clustering Algorithm
5. K means Clustering – Introduction
6. Elbow Method for optimal value of k in KMeans
7. Random Initialization Trap in K-Means
8. ML | K-means++Algorithm
9. Analysis of test data using K-Means Clustering in Python
10. Mini Batch K-means clustering algorithm
11. Mean-Shift Clustering
12. DBSCAN – Density based clustering
13. Implementing DBSCAN algorithm using Sklearn
14. Fuzzy Clustering
15. Spectral Clustering
16. OPTICS Clustering
17. OPTICS Clustering Implementing using Sklearn
18. Hierarchical clustering (Agglomerative andDivisive clustering)
19. Implementing Agglomerative Clustering using Sklearn
20. Gaussian Mixture Model
Reinforcement Learning:
1. Reinforcement learning
2. Reinforcement Learning Algorithm : Python Implementation usingQlearning
3. Introduction to Thompson Sampling
4. Genetic Algorithm for Reinforcement Learning
5. SARSA Reinforcement Learning
6. Q-Learning in Python
Dimensionality Reduction :
1. Introduction to Dimensionality Reduction
2. Introduction to Kernel PCA
3. Principal Component Analysis(PCA)
4. Principal Component Analysis with Python
5. Low-Rank Approximations
6. Overview of Linear Discriminant Analysis (LDA)
7. Mathematical Explanation of Linear Discriminant Analysis (LDA)
8. Generalized Discriminant Analysis (GDA)
9. Independent Component Analysis
10. Feature Mapping
11. Extra Tree Classifier for FeatureSelection
12. Chi-Square Testfor Feature Selection – Mathematical Explanation
13. ML | T-distributed Stochastic Neighbor Embedding (t-SNE) Algorithm
14. Python | How and where to apply Feature Scaling?
15. Parameters for Feature Selection
16. Underfitting and Overfitting in Machine Learning
Natural Language Processing :
1. Introduction to Natural Language Processing
2. Text Preprocessing in Python | Set – 1
3. Text Preprocessing in Python | Set 2
4. Removing stop words with NLTK in Python
5. Tokenize text using NLTK in python
6. How tokenizing text, sentence, words works
7. Introduction to Stemming
8. Stemming words withNLTK
9. Lemmatization withNLTK
10. Lemmatization with TextBlob
11. How to get synonyms/antonyms from NLTK WordNet in Python?
Neural Networks :
1. Introduction to Artificial Neutral Networks |Set 1
2. Introduction to Artificial Neural Network |Set 2
3. Introduction to ANN (Artificial Neural Networks)|Set 3 (Hybrid Systems)
4. Introduction to ANN |Set 4 (Network Architectures)
5. Activation functions
6. Implementing ArtificialNeural Network training process in Python
7. A single neuron neural network in Python
8. Convolutional Neural Networks
• Introduction to Convolution Neural Network
• Introduction to Pooling Layer
• Introduction to Padding
• Types of padding in convolution layer
• Applying Convolutional Neural Network on mnist dataset
9. Recurrent Neural Networks
• Introduction to Recurrent Neural Network
• Recurrent Neural Networks Explanation
• seq2seq model
• Introduction to Long Short Term Memory
• Long Short Term Memory Networks Explanation
• Gated Recurrent UnitNetworks(GAN)
• Text Generation using Gated Recurrent Unit Networks
10. GANs – Generative Adversarial Network
• Introduction to Generative AdversarialNetwork
• Generative Adversarial Networks (GANs)
• Use Cases of Generative AdversarialNetworks
• Building a Generative AdversarialNetwork using Keras
• Modal Collapse in GANs
11. Introduction to Deep Q-Learning
12. Implementing Deep Q-Learning using Tensorflow
ML – Deployment :
1. Deploy your Machine Learning web app (Streamlit) on Heroku
2. Deploy a Machine Learning Model usingStreamlit Library
3. Deploy Machine Learning Model using Flask
4. Python – Create UIs for prototyping Machine Learning model withGradio
5. How to Prepare Data Before Deploying a Machine Learning Model?
6. Deploying ML Models as API using FastAPI
7. Deploying Scrapy spider on ScrapingHub
ML – Applications :
1. Rainfall prediction using Linear regression
2. Identifying handwritten digits using Logistic Regression inPyTorch
3. Kaggle Breast Cancer WisconsinDiagnosis using Logistic Regression
4. Python | Implementation of Movie RecommenderSystem
5. Support Vector Machine to recognize facial features in C++
6. Decision Trees – Fake (Counterfeit) Coin Puzzle (12 Coin Puzzle)
7. Credit Card Fraud Detection
8. NLP analysis of Restaurant reviews
9. Applying Multinomial Naive Bayes to NLP Problems
10. Image compression using K-means clustering
11. Deep learning | Image Caption Generation using the Avengers EndGames Characters
12. How Does Google Use Machine Learning?
13. How Does NASA Use Machine Learning?
14. 5 Mind-Blowing Ways Facebook Uses Machine Learning
15. Targeted Advertising using Machine Learning
16. How Machine Learning Is Used by Famous Companies?
Misc :
1. Pattern Recognition | Introduction
2. Calculate Efficiency Of Binary Classifier
3. Logistic Regression v/s Decision Tree Classification
4. R vs Python in Datascience
5. Explanation of Fundamental Functions involved in A3C algorithm
6. Differential Privacy and Deep Learning
7. Artificial intelligence vs Machine Learning vs Deep Learning
8. Introduction to Multi-Task Learning(MTL)for Deep Learning
9. Top 10 Algorithms every Machine Learning Engineer should know
10. Azure Virtual Machine for Machine Learning
11. 30 minutes to machine learning
12. What is AutoML in Machine Learning?
13. Confusion Matrix in Machine Learning
Comments (0)
4.92
41 Reviews
Reviews (41)
Ananya Desai
2 Jun 2023 | 16:15
This course made machine learning feel easy to understand
Vihaan Khanna
5 Jun 2023 | 16:18
This course made Machine Learning incredibly easy to understand by breaking complex concepts into manageable pieces. The instructor's clear explanations and step-by-step approach helped me understand even the most complex algorithms easily
Anika Sharma
10 Jun 2023 | 16:19
I was pleasantly surprised by how clear and concise the explanations were in this course. The instructor did a great job of simplifying complex topics and making them accessible to learners of all levels.
Aarav Mehta
27 Jun 2023 | 16:20
As a beginner in the field of machine learning, I found this course extremely useful. The instructor's teaching style was engaging, and the practical exercises really helped solidify my understanding of key concepts.
Aanaya Khanna
2 Jul 2023 | 16:21
What I loved most about this course was the hands-on approach to learning. The instructor provided plenty of practical examples and exercises, which made it easier for me to apply the concepts learned in real-world scenarios
Aryan Joshi
10 Jul 2023 | 16:23
This course exceeded all my expectations. It not only covered a wide range of machine learning topics, but also provided practical insights on how to apply these concepts in real-world projects. Highly recommended for anyone wanting to delve deeper into the world of ML
Anaya Kapoor
16 Jul 2023 | 16:25
The course covers a wide range of machine learning algorithms, including regression, classification, clustering, and neural networks, providing a comprehensive overview of the field.
Kiran Mehta
25 Jul 2023 | 16:26
I appreciated the in depth exploration of popular ML libraries like TensorFlow and Scikit-Learn, which allowed me to gain practical experience working with real world datasets.
Aisha Trivedi
31 Jul 2023 | 16:29
I found the sections on data preprocessing and feature engineering to be particularly useful, as they provided practical strategies for cleaning and transforming data to improve model performance
Anika Kapoor
6 Aug 2023 | 16:30
I found some sections of the course to be informative, but overall, it didn't offer anything particularly groundbreaking
Arjun Singh
15 Aug 2023 | 16:32
I finally feel like I have a solid grasp of ML fundamentals
Anvi Joshi
24 Aug 2023 | 16:33
The course work was challenging but rewarding
Aarav Patel
7 Sep 2023 | 16:38
This course provided me with a solid foundation in machine learning. The instructor's clear explanations and engaging teaching style made complex topics more manageable. I now feel confident in implementing ML techniques in my projects
Ananya Trivedi
15 Sep 2023 | 16:40
The instructor's approachable teaching style and practical examples made the material much easier to digest
Avvya Bhatia
27 Sep 2023 | 16:41
The course content was well-organized and easy to follow, with each module building on the previous module. The instructor's clear explanations and real-world examples helped me understand complex algorithms and techniques
Advay Mehta
8 Oct 2023 | 16:44
I enjoyed the sections on ethical considerations in machine learning, which led to important discussions about bias, fairness, and transparency in AI algorithms.
Aaradhya Singh
20 Oct 2023 | 16:45
I was expecting more from the course based on the reviews, but it turned out to be just average
Arjun Joshi
25 Oct 2023 | 16:47
This course presented a comprehensive overview of advanced machine learning techniques. The instructor's expertise shone through in the detailed explanation of complex algorithms. Practical exercises and coding assignments reinforce key concepts
Zoya Khanna
6 Nov 2023 | 16:48
I had mixed feelings about the course; some parts were helpful, while others felt a bit too simplistic
Kiara Mehta
13 Nov 2023 | 16:50
This gave me a broader understanding of machine learning concepts and techniques, and the instructor's clear explanations and engaging teaching style made it much more manageable. I particularly enjoyed the practical exercises and real-world examples, which helped solidify my understanding of key concepts
Shahid
22 Nov 2023 | 16:52
I appreciated the practical tips shared by the instructor throughout the course, which helped me understand how machine learning techniques can be applied in real-world scenarios. The practical exercises were particularly helpful, as they allowed me to practice and reinforce my understanding of key concepts
Salman
1 Dec 2023 | 16:54
It provided a solid introduction to machine learning concepts, perfect for beginners looking to try their feet in this field.
Rohit Kumar
16 Dec 2023 | 16:56
I appreciate the practical examples provided throughout the course, which help reinforce theoretical concepts and make them more accessible
Aarav Sharma
26 Dec 2023 | 16:57
Although some parts of the course were informative, I felt that others could have been more engaging and interactive
Ananya Gupta
30 Dec 2023 | 16:59
I found the course materials to be well-organized and easy to follow, which helped me stay focused and engaged
Arjun Gupta
31 Dec 2023 | 17:00
The course provided a good balance of theory and practical application, allowing me to develop both conceptual understanding and practical skills
Avni Kapoor
2 Jan 2024 | 17:02
While the course covered a wide range of machine learning topics, I felt that some areas could have been explored in more depth
Bhavya Patel
7 Jan 2024 | 17:04
I would recommend this course to anyone looking to gain a basic understanding of machine learning principles and techniques
Chetan Verma
15 Jan 2024 | 17:06
The course was a little challenging at times, but I appreciate the opportunity to enhance my skills and push myself out of my comfort zone
Deepika Nair
28 Jan 2024 | 17:08
This course was a great investment in my career development, providing me with valuable skills that I can apply in various industries
Dev Desai
3 Feb 2024 | 17:09
The course content was updated and relevant, reflecting the latest advances and trends in the field of machine learning
Diya Reddy
6 Feb 2024 | 17:10
The interactive nature of the course with quizzes and assignments kept me engaged and motivated to learn
Esha Joshi
9 Feb 2024 | 17:12
The practical tips and advice shared by the instructor were invaluable, providing actionable insights that I could immediately apply to my own projects.
Farhana
12 Feb 2024 | 17:14
The instructor's passion for machine learning was evident in every lecture, bringing complex topics to life and generating curiosity
Gauri Mehta
16 Feb 2024 | 17:17
The course provided practical advice on how to approach machine learning projects from problem formulation and data collection to model evaluation and deployment
Gautam Yadav
19 Feb 2024 | 17:19
The platform was well designed and easy to navigate, had features like progress tracking and bookmarking that enhanced the learning experience
Ishaan Choudhary
22 Feb 2024 | 17:21
I found the self-paced nature of the course convenient, allowing me to study at a time that suited my schedule and pace my learning according to my needs.
Jaya Rathi
25 Feb 2024 | 17:22
The course provided valuable insights into the ethical implications of machine learning, prompting important discussions about bias, fairness, and accountability in AI systems
Kabir Kapoor
1 Mar 2024 | 17:24
It was the good practical learning opportunities provided by the course, with coding exercises and projects that allowed me to apply theoretical concepts in practical settings.
Kavya Sharma
5 Mar 2024 | 17:26
The instructors were knowledgeable, providing valuable insights and perspectives on machine learning topics
Krish Malhotra
29 Feb 2024 | 17:30
Provided practical strategies for applying machine learning techniques to real-world problems, with case studies and examples demonstrating how to overcome common challenges encountered in practice
