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Machine Learning with Python ( Beginner )
Chapter 1 Introduction
1. Why Machine Learning?
2. Why Python?
3. scikit-learn
4. Essential Libraries and Tools
5. Python 2 Versus Python 3
6. Versions Used in this Book
7. A First Application: Classifying Iris Species
8. Summary and Outlook

2. Chapter 2 Supervised Learning
1. Classification and Regression
2. Generalization, Overfitting, and Underfitting
3. Supervised Machine Learning Algorithms
4. Uncertainty Estimates from Classifiers
5. Summary and Outlook

3. Chapter 3 Unsupervised Learning and Preprocessing
1. Types of Unsupervised Learning
2. Challenges in Unsupervised Learning
3. Preprocessing and Scaling
4. Dimensionality Reduction, Feature Extraction, and Manifold Learning
5. Clustering
6. Summary and Outlook

4. Chapter 4 Representing Data and Engineering Features
1. Categorical Variables
2. Binning, Discretization, Linear Models, and Trees
3. Interactions and Polynomials
4. Univariate Nonlinear Transformations
5. Automatic Feature Selection
6. Utilizing Expert Knowledge
7. Summary and Outlook

5. Chapter 5 Model Evaluation and Improvement
1. Cross-Validation
2. Grid Search
3. Evaluation Metrics and Scoring
4. Summary and Outlook

6. Chapter 6 Algorithm Chains and Pipelines
1. Parameter Selection with Preprocessing
2. Building Pipelines
3. Using Pipelines in Grid Searches
4. The General Pipeline Interface
5. Grid-Searching Preprocessing Steps and Model Parameters
6. Grid-Searching Which Model To Use

7. Summary and Outlook
7. Chapter 7 Working with Text Data
1. Types of Data Represented as Strings
2. Example Application: Sentiment Analysis of Movie Reviews
3. Representing Text Data as a Bag of Words
4. Stopwords
5. Rescaling the Data with tf–idf
6. Investigating Model Coefficients
7. Bag-of-Words with More Than One Word (n-Grams)
8. Advanced Tokenization, Stemming, and Lemmatization
9. Topic Modeling and Document Clustering
10.Summary and Outlook

8. Chapter 8 Wrapping Up
1. Approaching a Machine Learning Problem
2. From Prototype to Production
3. Testing Production Systems
4. Building Your Own Estimator
5. Where to Go from Here
6. Conclusion

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The trainer is a working professional having 15years of work experience in cyber security and has delivered more than 60 batches till now and also himself is certified for CEH, ECSA, CHFI, LPT and CISSP. He provides complete practical training with live examples, we have received excellent feedback so far and our students have always referred us and preferred us for further certifications.Most of the students have done ECSA right immediately after completing the CEH training as they were so content with the training. Training we can assure that you will get the best industry standard quality training and also the flexibility of timings

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Python Training

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Python

"One of the best institutes in bengaluru, I have learned basic python, which has helped me a lot. Not to say that our faculty taught us in such a simple way, thank you very much. "

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Python Training

"The training for python was very nice complete practical's trainer is very knowledgeable and very friendly answered all our doubts. "

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5 out of 5 3 reviews

APC Learning Solutions https://www.urbanpro.com/assets/new-ui/institute-100X100.png M G Road 1
5.0053
APC Learning Solutions
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Python Training

"The best institute. Make me Zero to Hero. I got placed in IBM. Help me to build a successful career and even they are beside me. Very much supportive. Thank you Hackers University APC for teaching me well "

APC Learning Solutions
S

Python

"One of the best institutes in bengaluru, I have learned basic python, which has helped me a lot. Not to say that our faculty taught us in such a simple way, thank you very much. "

APC Learning Solutions
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Python Training

"The training for python was very nice complete practical's trainer is very knowledgeable and very friendly answered all our doubts. "

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