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| Item Details | Price | ||
|---|---|---|---|
Become an ML Ninja and get your dream job!
Language: Hinglish
Instructors: AcadBoost
Why this course?
Ever dreamed of becoming a Machine Learning Engineer and getting those fat packages? If yes, this is the right course for you!
The course starts off with mathematical and programmatical pre requisites, followed by basic ML algorithms (Regression, KNN, etc). Then we talk about more involved concepts related to Feature Engineering and Deep Learning.
Both theory and practical aspects of learning have been provided, and there is no way you are not going to be an ML Ninja once you're through!
| Course Overview | |||
| Course Overview (38:00) | |||
| Software Installation (7:00) | |||
| Python Basics | |||
| Python Basics 1 (28:00) | |||
| Python Basics 2 (22:00) | |||
| Numpy | |||
| Numpy (41:00) | |||
| Pandas | |||
| Pandas (34:00) | |||
| Matplotlib and Seaborn | |||
| 1. Matplotlib (20:00) | |||
| 2. Seaborn (23:00) | |||
| Probability and Statistics | |||
| 1. probability final (35:00) | |||
| 2. statistics1 (44:00) | |||
| Introduction to ML Algos | |||
| Machine learning Overview (29:00) | |||
| Linear Regression | |||
| 1. Linear Regression Theory (53:00) | Preview | ||
| 2. implementation of gradient descent (15:00) | |||
| 3. feature scaling (17:00) | |||
| 4. Linear regression Practical (16:00) | Preview | ||
| 5. Multivariate linear regression (21:00) | |||
| K Nearest Neighbours | |||
| KNN Theory (19:00) | |||
| KNN practical (18:00) | |||
| Decision Trees | |||
| Decision Tree Theory (21:00) | |||
| Decision Tree Use case | |||
| Logistic Regression | |||
| 1. Logistic Regression (16:00) | |||
| 2. Logistic Regression (31:00) | |||
| 3. Logistic Regression (31:00) | |||
| 4. Logistic Regression (31:00) | |||
| 5. Logistic Regression (14:00) | |||
| 6. Logistic Regression Practical (31:00) | |||
| Naive Bayes | |||
| 1. Naive Bayes (34:00) | |||
| 2. Naive Bayes real life use case (1,074:00) | |||
| 3. Gaussian Naive Bayes (20:00) | |||
| Scikit Learn | |||
| 1. Scikit learn tutorial (35:00) | |||
| 2. MNIST dataset (18:00) | |||
| 3. Logistic Regression scikit learn (13:00) | |||
| Feature Engineering | |||
| 1. Feature Engineering Over view (13:00) | |||
| 2. Outliers (18:00) | |||
| 3. Missing values (18:00) | |||
| 4. Missing value 2 (17:00) | |||
| 5. Categorical Encodings Theory (17:00) | |||
| 6. Encoding Practical (13:00) | |||
| 7. Feature slection theory | |||
| 8. Feature Selection Practical | |||
| 9. Data visualization application final (30:00) | |||
| 10. Final project on titanic data - 1 (35:00) | |||
| 11. Titanic - data pre processing and model creation (19:00) | |||
| 12. Telecom Customer Churn - Data visualization and EDA (31:00) | |||
| 13. Telecom Churn Analysis 2 (17:00) | |||
| Deep Learning | |||
| 1. Neural Networks 1 (31:00) | |||
| 2. Neural Network 2 (14:00) | |||
| 3. Activation Functions - Tan H (18:00) | |||
| 4. Activation Function ReLu (20:00) | |||
| 5. Activation Function - Softmax - (11:00) | |||
| 6. Deep Learning Practical (23:00) | |||
| 7. CNN intuition (42:00) | |||
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