Data Jango providing real time training in the fields of Data Science like Machine Learning which covers Supervised Learning, Unsupervised Learning and Reinforcement Learning, Deep Learning which covers Neural Networking Concepts like ANN, CNN and RNN using Keras, NLP, NLU which will help in building ai based applications.
Mode of Training: Online and Class Room(Hyderabad).
Duration: 3 Months
Push yourself, Because no one else is going to do IT for You — Success Quote
The course we will cover all necessary concepts to make a successful Data Scientist. The concepts we cover are Descriptive Statistics, Inferential Statistics, Basic Python, Pandas, NumPy, SciPy, Statistical Data Analysis, StatsModels, Scikit-Learn, Mathematics behind Machine Learning Algorithms (Gradient Descent, SVM, Kernal SVM, etc.), error analysis and most of the accuracy measures, techniques of fine-tuning a model.
Data Jango Training institute located in Madhapur, Hyderabad designed Data Science course in two phases, First one focuses on Machine Learning, Second one Focuses on Deep Learning.
| Course | Location | Mode of Class | Duration |
| Data Science | Hyderabad | Class-Room/Online | 3 Months |
745 Ratings
Trainer with 10+ years of experience
Certified Trainer
Real-Time Project Examples
Exercises after every Topic
Trainer support after completion of the course
Placement Assistance
Trainers with Industry Experience and IIIT Hyderabad
All Concepts and ML/DL Algorithms with Code Examples
• Central Tendency (mean, median and mode)
• Interquartile Range
• Variance
• Standard Deviation
• Z-Score/T-Score
• Co-variance
• Correlation
• Binomial Distribution
• Introduction to Probability
• Normal Distribution
• Bar Chart
• Histogram
• Box whisker plot
• Dot-plot
• Line plot
• Scatter Plot
• How to install python (anaconda)
• How to work with Jupyter Notebook
• How to work with Spyder IDE
• Compound data types
o Strings, Lists, Tuples, Sets, Dictionaries
• Control Flows
• Keywords (continue, break, pass)
• Functions (Formal/Positional/Keyword arguments)
• Predefined functions (range, len, enumerate, zip)
• One-dimensional Array
• Two-dimensional Array
• Predefined functions (arrange, reshape, zeros, ones, empty, eye, linespace)
• Basic Matrix operations
o Slicing, indexing, Looping, Shape Manipulation, Stacking
o Scalar addition, subtraction, multiplication, division, broadcasting
o Matrix addition, subtraction, multiplication, division and transpose, broadcasting
• Series
• DataFrame
• df.GroupBy
• df.crosstab
• df.apply
• df.map
• df.mapapply
• Central Limit Theorem
• Confidence Interval and z-distribution table
• Statistical Significance
• Hypothesis testing
• P-value
• One-tailed and Two-tailed Tests
• Chi-Square Goodness of Fit Test
• F- Statistic (ANOVA)
• Skewness, Kurtosis
• Train/Test split – Data snooping bias
• Statistical Data Analysis
• Fixing missing values
• Finding outliers
• Data quality check
• Feature transformation
• Data Visualization (Matplotlib, Seaboarn)
o Categorical to Categorical
o Categorical to Quantitative
o Quantitative to Quantitative
• Bi-Variate data analysis (Hypothesis Testing)
o Categorical and Quantitative (ANOVA)
o Categorical to Categorical (Chi-Square)
o Quantitative to Categorical (Chi-Square)
o Quantitative to Quantitative (Correlation)
• What is regression?
• Simple linear regression
• Linear Regression – a statistics perspective (statsmodels – OLS)
• Evaluation metrics (R-Squre, Adj R-Squre, MSE, RMSE)
• Mean centralization and its use in multiple linear regression
• Multiple linear regression
• P – Value based feature selection methods (Backward, Forward and Mixed)
• Linear regression assumptions (linear relations – fitted vs residuals plot, homoscedasticity, normal distribution of error term, serial correlation, multicollinearity)
• Q-Q Plot, Shapiro Wilk test – different ways to check normality of data.
• Data transformation techniques.
• Label Encoding
• One-Hot (dummy variable) encoding
• Dummy variable trap
• Scikit-Learn → Custom Transformers
• Scikit-Learn → Pipeline
• Normal Equation (Linear Algebraic way of solving linear equation)
• Gradient Descent (Calculus way of solving linear equation)
• Multiple Linear Regression (SGD Regressor)
• Feature Scaling ( Min-Max vs Mean Normalization)
• Feature Transformation
• Polynomial Regression
• Train/Validation/Test split
• K-Fold Cross Validation
• The Problem of Over-fitting (Bias-Variance tread-off)
• Learning Curve
• Regularization (Ridge, Lasso and Elastic-Net)
• Feature selection
• Hyper Parameter Tuning (GridSearchCV, RandomizedSearchCV)
• Pickle (pkl file)
• Model load from pkl file and prediction
• Logistic Regression Algorithm (SGD Classifier)
• Accuracy measurements – handling imbalanced dataset
o Accuracy score
o Confusion matrix
o Precision
o Recall
o Precision – Recall tread off curve
o ROC curve
o AUC score
• Multi-class Classification
o One-vs-One
o One-vs-All
o Softmax regression classifier
• Multi-label Classification
• Multi- output Classification
• SVM Classifier (Soft/Hard – Margin)
• Linear SVM
• Non-Linear SVM
• Kernel Trick (mathematics behind kernel trick)
• Kernel SVM
• SVM Regression
• K-means
• Hierarchical
• How to use unsupervised outcome as support to solve supervised problem.
• PCA
• Math behind PCA – Eigen vectors, eigen values, covariance matrix.
• Choosing Right Number of Dimensions or Principal Components
• Incremental PCA
• Kernel PCA
• Regression Trees vs Classification Trees
• Entropy
• Gini Index
• Information Gain
• Tree pruning
• Voting Classifiers (Heterogeneous Ensemble Models)
• Homogeneous Ensemble Models
o Random Forest
o Bagging
o Pasting
o Introduction to Boosting (Ada, Gradient)
• Bayes Theorem
• Naive Bayes Algorithm
• Introduction to Text Analytics
• Tokenization
• Text Normalization, stemming, lemmatization
• Bag of words mode
• Anomaly vs Classification
• Credit Card Fraud detection – Anomaly Detection Algorithm
• Assumptions of normality
• Overview of Hadoop architecture
• Overview of YARN architecture
• Map-Reduce example
• Overview of Spark Context (–master YARN)
• Resilient Distributed Datasets (RDDs)
• RDD Operations (Transformations, Actions)
• Spark DataFrames
• Spark ML model with Pipeline
• Classification model, MulticlassMetrics
• Perceptron, Sigmoid Neuron
• Neural Network model representation
• How it works
• Forward-Propagation
• Back-Propagation