Machine Learning Course In Mumbai Machine Learning Training Course In Mumbai & Thane - Itvedant

Machine learning helps software applications to become more accurate in predicting outcomes.

'Simple and powerful are the two words which define the most modern programming language - Python.'

Machine Learning with Python course fees: Affordable Fees

"Knowledge is always free we charge for trainers time. And if you are unsatisfied at the end of the course or if you feel we failed to deliver whatever is promise then, we refund you your complete fees."

To know more about ML: Visit  https://www.itvedant.com/python-training-course and to know more about Data Science - Visit https://www.itvedant.com/data-science-course

Google Review of Itvedant - Itvedant is known as the best class for python training

1. Introduction

• What is Data Science
• Who is a Data Scientist
• Types of Data
• Machine learning introduction
• Deep learning and AI introduction
2. Numpy

• Creating numpy arrays
• Properties of array
• Aggregate functions
• Numpy functions
• Indexing and slicing
• Vectorization
• Boolean indexing
3. Pandas

• Data Frame
• Reading data from various source
• Data Frame properties
• Data Frame indexing and slicing
• Data Frame functions
• Pandas functions
4. Visualization

• Matplotlib line plot
• Scatter plot
• Count plot
• Histogram
• Pie chart
• Subplots
• Pandas plot
• Seaborn plots
5. Descriptive statistics

• Introduction to statistics
• Mean
• Median and mode
• Variance and Standard deviation
• Covariance and correlation coefficient
• Normal distribution
6. Probablity

• Introduction to probability
• Theoretical and Frequentest approach
• Dependent and Independent events
• Conditional probability
• Bayes Theorem
• Binomial distribution
• Standardization and calculate Z score
7. Inferential statistics

• Introduction to inferential statistics
• Sampling techniques
• Central limit theorem
• Hypothesis testing
8. Machine learning

• Linear regression
• OLS
• Implementation with Scikit learn
• Evaluation metrics (MSE, R2)
9. Regularization

• Cost functions
• Ridge and Lasso regularization
• Hyper-parameter tuning
10. EDA and Preprocessing

• Handling missing values
• Handling outliers Handling skewness
• Scaling
• Label Encoding
• Feature engineering
11. Feature selection

• Co-relation coefficient and heatmap
• Chi square test
• ANNOVA
• Random Feature elimination
• PCA
12. Classification

• Logistic regression
• Sigmoid function
• Multi-class regression
• Confusion matrix
• Accuracy, Recall, Precision, F1 score
• AUC-ROC
13. Descision Tree

• Introduction to decision tree
• Terminologies
• Entropy and Gini index
• Visualizing decision tree
• Handling over fitting (Pruning)
14. Ensembling

• Stacking

• Gradient boosting algorithm
• XGBoosting
16. Clustering

• Introduction to unsupervised learning
• Introduction to clustering
• K-Means
• Hierarchical clustering
17. Support Vector Machines

• SVM
• Soft margin
• SVM kernels
• Multi-class SVM

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