Machine Learning Course Training in Hyderabad

1. Importance of Data in 21st Century
2. Types of Data and its usage
3. Python Crash course (using IDLE)
‍
● Data Types
● Conditional Statements
● Control Statements
● Functions
‍
4. What is Machine Learning and types of Learning
5. Anaconda Environment Setup and its usage .

1. Numpy
● Creating arrays, Difference between List and Array
● Accessing Elements, Slicing, Concatenation
● Universal Functions, Shape Manipulation
● Automatic Reshaping, Vector Stacking.
2. Pandas
● Pandas DataStructures
● Indexing, Selecting Data, Slicing functions
● Some Useful DataFrame Functions
● Handling Missing Values in DataFrame
● Time Series Analysis
3. Data Visualization Libraries
● Matplotlib
● Plotly
● Basic Plotting with Seaborn
● Projects on Data Analysis – Google Analysis, Market Analysis
4. Sklearn usage
● Playing with Scikit-learn, Understanding classes in Scikit-learn

Machine Learning Fundamentals
• What is machine learning?
• How Machine Learning works?
• Applications of machine learning
• Different types of machine learning
• How do we know machines are learning right?
• Different stages of machine learning projects.Data Transformation and Preprocessing
• Handling Numeric Features
• Feature Scaling
• Standardization and Normalization
• Handling Categorical Features
• One Hot Encoding, pandas get_dummies
• Label Encoding
• More on different encoding techniquesTrain,Test and Validation Split
• Simple Train and Test Split
• Drawbacks of train and test split
• K-fold cross validation
• Time based splittingOverfitting And Underfitting
• What is overfitting ?
• What causes overfitting?
• What is Underfitting ?
• What causes underfitting ?
• What are bias and Variance ?
• How to overcome overfitting and underfitting problems ?

Regression
• Introduction to Linear Regression
• Understanding How Linear Regression Works
• Maths behind Linear Regression
• Ordinary Least Square
• Gradient Descent
• R – square
• Adjusted R-square
• Polynomial Regression
• Multiple Regression
• Performance Measures – MSE, RMSE, MAE
• Assumption of Linear Regression
• Ridge and Lasso regression
• RFE (Recursive Feature elimination)
Hands On – Problem formulation and Case Study on Hotstar, Netflix, And housing prices Dataset
Classification
Logistic regression
• Introduction to classification problems
• Introduction to logistic regression
• Why the name regression ?
• The sigmoid function
• Log odds
• Cost function
• Feature importance and model interpretability
• Collinearity of features
• Feature engineering for non-linearly separable data
Performance Metrics for Classification Algorithms
• Accuracy Score
• Confusion Matrix
• TPR, FPR, FNR, TNR
• Precision – Recall
• F1-Score
• ROC Curve and AUC
• Log LossHands On – Real World Case Study on IBM HR Employee Attrition datasetK Nearest Neighbors

• Introduction to KNN
• Effectiveness of KNN
• Distance Metrics
• Accuracy of KNN
• Effect of outlier on KNN
• Finding the k Value
• KNN on regression
• Where not to use KNN
Hands On – Different case study on KNN
Natural Language Processing
• Introduction to NLP
• Converting Text to vector
• Data Cleaning
• Preprocessing Text Data – Stop word removal, Stemming , Tokenization, Lemmatization
• Collecting Data from the web
• Developing a Classifier
• Building Pipelines for NLP projects
• Uni-grams,bi-grams and n-grams
• tf-idf
• Word2Vec
Hands On – Text Summarization, WebScraping for data, Sentiment Analysis, Topic Modelling, Text Summarization and Text Generation
Naive Bayes
• Refresher on conditional Probability
• Bayes Theorem
• Examples on Bayes theorem
• Exercise problems on Naive Bayes
• Naive Bayes Algorithm
• Assumptions of Naive Bayes Algorithm
• Laplace Smoothing
• Naive Bayes for Multiclass classification
• Handling numeric features using Naive Bayes
• Measuring performance of Naive Bayes
Hands On – Working on spam detection and Amazon Food Review dataset
Support Vector Machines
• Introduction to SVM
• What are hyperplanes ?
• Geometric intuition
• Maths behind svm’
• Loss Function
• Kernel trick
• Polynomial kernel, rbf and linear kernels
• SVM Regression
• Tuning the parameter
• GridSearch and RandomizedSearch
• SVM Regression
Hands On – Case Study SVM on Social network ADs and Gender recognition from voice datasetDecision Tree
• Introduction to Decision Tree
• Homogeneity and Entropy
• Gini Index
• Information Gain
• Advantages of Decision Tree
• Preventing Overfitting
• Advantages And Disadvantages
• Plotting Decision Trees
• Plotting feature importance
• Regression using Decision Trees
Hands-On – Decision Tree on US Adult income dataset
Ensemble Learning
• Introduction to Ensemble Learning
• Bagging (Bootstrap Aggregation)
• Constructing random forests
• Runtime
• Case study on Bagging
• Tuning hyperparameters of random forest(GridSearch, RandomizedSearch)
• Measuring model performance
• Boosting
• Gradient Boosting
• Adaboost and XGBoost
• Case study on boosting trees
• Hyperparameter tuning
• Evaluating performance
• Stacking Models
Hands-On – Talking Data Ad Tracking Fraud Detection case study

Clustering
• Introduction to unsupervised learning
• Applications of Unsupervised Learning
• Kmeans Geometric intuition
• Maths Behind Kmeans
• Kmeans in presence of outliers
• Kmeans random initialization problem
• Kmeans++
• Determining the right k
• Evaluation metrics for Kmeans
• Case study on Kmeans
• Hierarchical Clustering
• Agglomerative and Divisive
• Denodgrams
• Case study on hierarchical clustering
• Segmentation
• Case Study on Segmentation
• DBSCAN – Density based clustering
• MinPts and Eps
• Core Border and Noise Points
• Advantages and Limitation of DBSCAN
• Case Study on DBSCAN clustering
Hands On – Applying Unsupervised models on Retail data and mall customer datasetDimensionality Reduction Techniques
• What are dimensions?
• Why is high dimensionality a problem ?
• Introduction to MNIST dataset with (784 Dimensions)
• Into to Dimensionality reduction techniques
• PCA (Principal Component Analysis) for dimensionality reduction
• t-sne (t-distributed stochastic neighbor embeddingHands-on: Applying Dimensionality Reduction on MNIST data

• Introduction
• Markov Decision Process
• Expected Return
• Policy and Value Function
• Q-Learning
• Exploration vs Exploitation
• OpenAI Gym and python for Q-learning
• Training Q-Learning Agent
• Watching Q-Learning Play GamesHands On – Working with OpenAI Gym and Q-Learning