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Machine learning with python
This document outlines a machine learning training program offered by Bepec Solutions that covers topics such as Python, statistics, machine learning algorithms including linear regression, logistic regression, decision trees, random forests, neural networks, support vector machines, naive bayes, text mining, and KNN. The training includes fundamentals of TensorFlow and scikit-learn libraries and covers supervised, unsupervised, and reinforcement learning. Hands-on projects are included to apply the algorithms to real-world datasets. Post-training support is also provided.
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Machine learning with python
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- 2. W H O WE A R E M a k e a c h a n g e t o e a c h a n d e v e r y p r o f e s s i o n a l b y e x p o s i n g t h e m t o f u t u r e c o u r s e s a n d e n h a n c i n g t h e i r s k i l l s w i t h c e r t i f i c a t i o n s W H A T W E D O T r a i n i n g s t a r t s w i t h t h e f u n d a m e n t a l s o f T e n s o r F l o w l i b r a r y w h i c h i n c l u d e s v a r i a b l e s , m a t r i c e s , a n d v a r i o u s d a t a s o u r c e s . BEPEC SOLUTIONS. Register.Master.Goahead
- 3. GETTING STARTED STATISTICS Why Statistics? Introductionto Statistics Various Data Types Various Measurement Scales Exploratory Data Analysis Measures of Central Tendency Measures of Dispersion Practical Approach on EDA Features of Data Distribution Data Frames Comparing Populations Sample Spaces BEPEC SOLUTIONS. Register.Master.Goahead
- 4. Confidence Interval Properties ofProbability Conditional Probability Independent Events Random Variables Discrete and Continuous Binomial Distribution Normal Distribution Probability Distribution Simple Random Sample Sampling Central Limit Theorem Sampling Distribution Simulated Sampling BEPEC SOLUTIONS. Register.Master.Goahead
- 5. Point Estimation Confidence Intervalfor Means Confidence Interval for Variance Sample Size and Margin Errors Hypothesis Tests for Proportion Null Hypothesis Alternate Hypothesis Alpha Risk and Beta Risk Significance of p-value Hands-On on Hypothesis BEPEC SOLUTIONS. Register.Master.Goahead
- 6. MACHINE LEARNING WITH PYTHON Introto Machine Learning Types of Machine Learning Supervised Learning Unsupervised Learning Reinforcement Learning Application of Machine Learning Evolution of Machine Learning Types of Predictions Response Classification Neuron Based Classification Simple Linear Regression BEPEC SOLUTIONS. Register.Master.Goahead
- 7. Case Study onLinear Regression Project on Linear Regression R-Squared Value Improving the Accuracy Multiple Linear Regression Simple Logistic Regression Confusion Matrix Multiple Logistic Regression Various Families in Logistic Decision Trees Theory Cancer Prediction with Decision Improving the Accuracy Cross-Validation of Tree Project on Decision Tree BEPEC SOLUTIONS. Register.Master.Goahead
- 8. Random Forest Project onRandom Forest K-Means Theory Various Clusters Practical of K-Means Project on K-Means Intro to K-NN Case Study on K-NN Project on K-NN Concept of Support Vectors Hyperplanes in SVM Implementation of Kernels BEPEC SOLUTIONS. Register.Master.Goahead
- 9. Case-Study on SVM Projecton SVM Introduction to Deep Learning Concept of Artificial Neurons Building Feed Forward NeuralNet Impact of Hidden Layers Gradient Descent Weights Update BEPEC SOLUTIONS. Register.Master.Goahead
- 10. LINEAR REGRESSION LOGISTIC REGRESSION DECISIONTREE RANDOM FOREST NEURAL NETWORK SUPPORT VECTOR MACHINE NAIVE BAYES TEXT MINING KNN K-MEANS REGRESSION TREES SUPPORT VECTOR REGRESSION BEPEC SOLUTIONS. Register.Master.Goahead MACHINE LEARNING ALGORITHMS
- 11. N L P Introto Text Mining and NLP Concept of Document Corpus Bag-of-Words Stemming of Words Creating Document Term Matrix Analysing Unstructured Data 10,000 Reviews from Flipkart Cleaning the Data Plotting WordCloud Implementing Naive Bayes Bayesin Rule To classify Reviews Improving the Accuracy BEPEC SOLUTIONS. Register.Master.Goahead
- 12. MACHINE LEARNING WITH SCIKITLEARN Gentle Introduction Installing scikit-learn Our first machine learning method – linear classification Evaluating our results Machine learning categories Important concepts related to machine learning BEPEC SOLUTIONS. Register.Master.Goahead
- 13. NONLINEAR CLASSIFICATION AND REGRESSIONWITH DECISION TREES ADAVANCED FEATURES Feature extraction Feature selection Model selection Grid search Parallel grid search Decision trees Training decision trees Decision trees with scikit-learn BEPEC SOLUTIONS. Register.Master.Goahead
- 14. PROJECTS POST TRAINING EXAMINATION RESUMING PREPARATION INTERVIEW GUIDANCE BEPEC SOLUTIONS.
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