What is Machine Learning?
Why learn Machine Learning?
Prerequisites for learning Machine Learning.
What we will learn in Machine Learning?
- Application of Machine Learning
- Supervised vs Unsupervised Learning
- Python libraries suitable for Machine Learning
- Linear Regression
- Non-linear Regression
- Model evaluation methods
- NumPy
- Scipy
- Matploat
- Pandas
- NumPy - Ndarray Object
- NumPy - Array Attributes
- NumPy - Array Creation Routines
- NumPy - Array from Numerical Ranges
- NumPy - Advanced Indexing
- NumPy – Broadcasting
- NumPy - Iterating over Array
- NumPy - Array Manipulation
- NumPy - Binary Operators
- NumPy - String Functions
- NumPy - Mathematical Functions
- NumPy - Arithmetic Operations
- NumPy - Statistical Functions
- NumPy - Sort, Search & Counting Functions
- NumPy - Copies & Views
- NumPy - Matrix Library
- NumPy - Linear Algebra
- SciPy Sub packages
- SciPy Installation
- SciPy Cluster
- SciPy Constant
- SciPy FFTpack
- SciPy Integrate
- SciPy Interpolation
- SciPy I/O
- SciPy Linear Algebra
- SciPy Ndimage
- SciPy Optimize
- SciPy Stats
- SciPy Sparse Matrix
- SciPy Spatial
- SciPy ODR
- SciPy
- Matplotlib - Introduction
- Matplotlib - Environment Setup
- Matplotlib - Anaconda distribution
- Matplotlib - Jupyter Notebook
- Matplotlib - Pyplot API
- Matplotlib - Simple Plot
- Matplotlib - PyLab module
- Object-oriented Interface
- Matplotlib - Figure Class
- Matplotlib - Axes Class
- Matplotlib - Multiplots
- Matplotlib - Subplots() Function
- Matplotlib - Subplot2grid() Function
- Matplotlib - Grids
- Matplotlib - Formatting Axes
- Matplotlib - Setting Limits
- Setting Ticks and Tick Labels
- Matplotlib - Twin Axes
- Matplotlib - Bar Plot
- Matplotlib - Histogram
- Matplotlib - Pie Chart
- Matplotlib - Scatter Plot
- Matplotlib - Contour Plot
- Matplotlib - Quiver Plot
- Matplotlib - Box Plot
- Matplotlib - Violin Plot
- Three-dimensional Plotting
- Matplotlib - 3D Contour Plot
- Matplotlib - 3D Wireframe plot
- Matplotlib - 3D Surface plot
- Matplotlib - Working With Text
- Mathematical Expressions
- Matplotlib - Working with Images
- Matplotlib – Transforms
- Pandas Series
- Pandas Series.map()
- Pandas Series.std()
- Series.to_frame()
- Series.unique()
- Series.value_counts()
- Pandas DataFrame
- DataFrame.append()
- DataFrame.apply()
- DataFrame.aggregate()
- DataFrame.assign()
- DataFrame.astype()
- DataFrame.count()
- DataFrame.cut()
- DataFrame.describe()
- DataFrame.drop_duplicates()
- DataFrame.groupby()
- DataFrame.head()
- DataFrame.hist()
- DataFrame.iterrows()
- DataFrame.join()
- DataFrame.mean()
- DataFrame.melt()
- DataFrame.merge()
- DataFrame.pivot_table()
- DataFrame.query()
- DataFrame.rename()
- DataFrame.sample()
- DataFrame.shift()
- Pandas Series
- Pandas Series.map()
- Pandas Series.std()
- Series.to_frame()
- Series.unique()
- Series.value_counts()
- Pandas DataFrame
- DataFrame.append()
- DataFrame.apply()
- DataFrame.aggregate()
- DataFrame.assign()
- DataFrame.astype()
- DataFrame.count()
- DataFrame.cut()
- DataFrame.describe()
- DataFrame.drop_duplicates()
- DataFrame.groupby()
- DataFrame.head()
- DataFrame.hist()
- DataFrame.iterrows()
- DataFrame.join()
- DataFrame.mean()
- DataFrame.melt()
- DataFrame.merge()
- DataFrame.pivot_table()
- DataFrame.query()
- DataFrame.rename()
- DataFrame.sample()
- DataFrame.shift
- DataFrame.sort()
- DataFrame.sum()
- DataFrame.to_excel()
- DataFrame.transform()
- DataFrame.transpose()
- DataFrame.where()
- Pandas Operation
- Add column to DataFrame columns
- DataFrame to Numpy Array
- Pandas DataFrame to CSV
- Pandas Reading Files
- Pandas Concatenation
- Data operations
- Data Operations
- Data Processing
- DataFrame.corr()
- DataFrame.dropna()
- DataFrame.fillna()
- DataFrame.replace()
- DataFrame.iloc[]
- DataFrame.isin()
- DataFrame.loc[]loc vs iloc
- Pandas Cheat Sheet
- Pandas Index
- Multiple Index
- Pandas Reindex
- Reset Index
- Set Index()
- Pandas NumPy
- Boolean indexing
- Concatenating data
- Pandas vs NumPy
- Pandas Time Series
- Pandas Time Offset
- Pandas Time Periods
- Convert string to date
- Pandas Plot
- See yourself doing some new typical projects in Machine Learning
Benefits of joining Machine Learning Training Course with Conaxweb Solutions.
- Job Oriented Training Program available in Offline/ Online mode.
- Curated Study Material with Project Case Study.
- Final year project & documentation guidance
- Training by Experienced faculty.
- Doubt sessions and real IT working experience.
- Interview Preparation material and Career Consulting.
- Certification on training completion.
Teaching Mode: Online / Offline
Address: 259, 1A, Vinoba Nagar, Naini, Prayagraj, Uttar Pradesh 211010
Contact number : 05323557581, +91-9555433745
